visual-search/report/coursework.lyx
2019-12-03 13:46:34 +00:00

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\begin_body
\begin_layout Title
Visual Search Coursework
\end_layout
\begin_layout Author
Andy Pack (6420013)
\end_layout
\begin_layout LyX-Code
\begin_inset Newpage pagebreak
\end_inset
\end_layout
\begin_layout Section*
Abstract
\end_layout
\begin_layout Standard
The efficacy of various descriptors for visual search was investigated by
applying them to the MSRCv2 dataset.
Descriptors of varying complexity were considered from the less discriminative
global colour histogram to spatial techniques that consider both colour
and shape information.
\end_layout
\begin_layout Standard
Following these results the use of principal component analysis was used
to lower the dimensionality of descriptors and aid in extraction and processing.
The associated Mahalanobis distance tended to provide an improvement to
a standard Euclidean distance measure but the L1 generally outperformed
the both.
\end_layout
\begin_layout Standard
Spatial texture techniques, specifically a combination of colour and texture,
were found to be the best performing with PCA providing extra performance
when applied to the correct degree.
Internal parameters were generally found not to increase performance linearly
but achieve a peak before degrading.
\end_layout
\begin_layout LyX-Code
\begin_inset CommandInset toc
LatexCommand tableofcontents
\end_inset
\end_layout
\begin_layout Quotation
\begin_inset Newpage pagebreak
\end_inset
\end_layout
\begin_layout Section
Introduction
\end_layout
\begin_layout Standard
An application of computer vision and visual media processing is that of
visual search, the ability to quantitatively identify features of an image
such that other images can be compared and ranked based on similarity.
\end_layout
\begin_layout Standard
These measured features can be arranged as a data structure or descriptor
and a visual search system can be created through the extraction and comparison
of these descriptors.
This is an example of content based image retrieval or CBIR.
\end_layout
\begin_layout Standard
Visual search is used in consumer products to generate powerful results
such as Google Lens and Google reverse image search.
It also has applicability as smaller features of products such as 'related
products' results.
\end_layout
\begin_layout Subsection
Extraction
\end_layout
\begin_layout Standard
When arranged as three 2D arrays of intensity for each colour channel, an
image can be manipulated and measured to identify features using colour
and shape information.
The methods for doing so have varying applicability and efficacy to a visual
search system, many also have variables which can be tuned to improve performan
ce.
\end_layout
\begin_layout Subsection
Comparison
\end_layout
\begin_layout Standard
Typically a descriptor is a single column vector of numbers calculated about
an image.
This vector allows an image descriptor to plotted as a point in a feature
space of the same dimensionality as the vector.
Images that are close together in this feature space will indicate that
they have similar descriptors.
Methods for calculating the distance will determine how images are ranked.
\end_layout
\begin_layout Section
Descriptors
\end_layout
\begin_layout Subsection
Average Colour
\end_layout
\begin_layout Standard
Average colour represents one of the most basic descriptors capable of being
calculated about an image, an array of three numbers for the average red,
green and blue intensity values found in the image.
\end_layout
\begin_layout Standard
These three numbers represent nothing about the distribution of colour throughou
t the image and nothing regarding edge and shape information.
The lack of either hinders it's practicality for real world applications
and as a result it is not used as a descriptor on it's own in this paper.
Instead average colour will be used as a sub-descriptor during spatial
colour investigations aided by its low dimensionality.
\end_layout
\begin_layout Subsection
Global Colour Histogram
\end_layout
\begin_layout Standard
A global colour histogram extracts colour distribution information from
an image which can be used as a descriptor.
\end_layout
\begin_layout Standard
Each pixel in an image can be plotted as a point in it's 3D colour space
with the axes being red, green and blue intensity values.
Visually inspecting this colour space will provide information about colour
scattering found throughout the image.
As different resolutions of images will produce datasets of different sizes
in the feature space, a descriptor must be devised that transforms this
data into a resolution agnostic form which can be compared.
\end_layout
\begin_layout Standard
Each axes is partitioned into
\begin_inset Formula $q$
\end_inset
divisions so that a histogram can be calculated for each colour channel.
Each channel's intensity value,
\begin_inset Formula $val$
\end_inset
, can be converted into an integer bin value using equation
\begin_inset CommandInset ref
LatexCommand ref
reference "eq:integer-bin-calc"
plural "false"
caps "false"
noprefix "false"
\end_inset
, where floor strips a float value into an integer by truncating all values
past the decimal point.
\end_layout
\begin_layout Standard
\begin_inset Formula
\begin{equation}
bin\:val=floor\left(q\cdotp\frac{val}{256}\right)\label{eq:integer-bin-calc}
\end{equation}
\end_inset
\end_layout
\begin_layout Standard
This allows each pixel to now be represented as three 'binned' values, a
full RGB colour space has been reduced to three colour histograms, one
for each channel.
In order to arrange this as a descriptor each point should be further reduced
to a single number so that a global histogram can be formed of these values.
This is done by taking decimal bin integers and concatenating them into
a single number in base
\begin_inset Formula $q$
\end_inset
.
For an RGB colour space, each pixel can be augmented as shown in equation
\begin_inset CommandInset ref
LatexCommand ref
reference "eq:base-conversion"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\begin_inset Formula
\begin{equation}
pixel\:bin=red\:bin\cdotp q^{2}+green\:bin\cdotp q^{1}+blue\:bin\cdotp q^{0}\label{eq:base-conversion}
\end{equation}
\end_inset
\end_layout
\begin_layout Standard
Calculating a histogram of each pixel's bin value will function as a descriptor
for the image once normalised.
This normalisation will remove the effect of changing resolutions of image.
\end_layout
\begin_layout Standard
Each descriptor plots an image as a point in a
\begin_inset Formula $q^{3}$
\end_inset
-dimensional feature space where similarity can be computed using a suitable
distance measure (L2 norm for example).
\end_layout
\begin_layout Subsubsection
Efficacy
\end_layout
\begin_layout Standard
The advantage of global colour histogram over the average RGB descriptor
is that amounts of colours are now represented in the descriptor.
Clusters of similar colours representing objects or backgrounds will be
captured and can be compared.
\end_layout
\begin_layout Standard
A global histogram, however, holds no spatial colour information, this is
lost by plotting the pixels in their colour space.
\end_layout
\begin_layout Standard
This suggests that performing a pixel shuffling operation on the image will
not affect the extracted descriptor which has implications on the adequacy
of the methodology for a visual search system.
\end_layout
\begin_layout Subsection
Spatial Colour
\end_layout
\begin_layout Standard
Spatial techniques involve descriptors that represent information from different
areas of the image.
This is done by dividing the image into a grid of cells and then calculating
individual 'sub-descriptors' which are concatenated into a global image
descriptor.
\end_layout
\begin_layout Standard
These sub-descriptors can be calculated using any appropriate method however
a main consideration should be the dimensionality of the final descriptor.
This can be calculated using the following equation,
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
D_{total}=W\cdotp H\cdotp D_{sub-descriptor}
\]
\end_inset
\end_layout
\begin_layout Standard
Where
\begin_inset Formula $W$
\end_inset
and
\begin_inset Formula $H$
\end_inset
refer to the number of columns and rows of the determined grid respectively.
\end_layout
\begin_layout Standard
It would be feasible to calculate a colour histogram however this already
generates a descriptor of
\begin_inset Formula $q^{3}$
\end_inset
dimensionality, where
\begin_inset Formula $q$
\end_inset
is the number of bins.
\end_layout
\begin_layout Standard
For example using a
\begin_inset Formula $q$
\end_inset
value of 4 and a spatial grid of 6 x 4 would produce a descriptor in 1536
dimensions, while a
\begin_inset Formula $q$
\end_inset
of 6 with a a grid of 10 x 6 is 12,960 dimensional.
\end_layout
\begin_layout Standard
This is an extremely high value and will increase the time taken to calculate
and compare descriptors.
\end_layout
\begin_layout Standard
For a spatial colour descriptor the average RGB values for each cell can
be used as these sub descriptors will be three dimensional reducing the
total size.
\end_layout
\begin_layout Subsubsection
Efficacy
\end_layout
\begin_layout Standard
Computing a spatial colour descriptor can increase performance when highlighting
the difference to a colour histogram.
While a colour histogram will describe how many of each colour is present
in an image, spatial colour techniques of the type described above will
indicate the colours found in each area of the image.
Considering an image of a cow in a field, the colour histogram will identify
and count the brown pixels of the cow and the green pixels of the field,
spatial colour techniques will identify an area of brown surrounded by
an area of green.
\end_layout
\begin_layout Subsection
Spatial Texture
\end_layout
\begin_layout Standard
Spatial texture replaces the colour sub-descriptor from before with a descriptor
that reflects the texture found in the image as described by the edges
that can be detected.
\end_layout
\begin_layout Subsubsection
Edge Detection
\end_layout
\begin_layout Standard
Edges can be detected in an image by finding areas where neighbouring pixels
have significantly different intensities.
\end_layout
\begin_layout Standard
Mathematically this can be seen as taking the first derivative of the image
by convolving it with a Sobel filter.
The Sobel filters are a pair of 3x3 kernels, one for each axes (see figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:3x3-Sobel-filter"
plural "false"
caps "false"
noprefix "false"
\end_inset
), which approximates the gradient of the grey-scale intensity of an image.
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Formula $S_{x}=\begin{bmatrix}-1 & 0 & +1\\
-2 & 0 & +2\\
-1 & 0 & +1
\end{bmatrix}$
\end_inset
\begin_inset space \qquad{}
\end_inset
\begin_inset Formula $S_{y}=\begin{bmatrix}+1 & +2 & +1\\
0 & 0 & 0\\
-1 & -2 & -1
\end{bmatrix}$
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
3x3 Sobel filter kernels for
\begin_inset Formula $x$
\end_inset
and
\begin_inset Formula $y$
\end_inset
axes
\begin_inset CommandInset label
LatexCommand label
name "fig:3x3-Sobel-filter"
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The results of convolving each filter with the image are two images that
express the intensity of edges in that axes.
\end_layout
\begin_layout Standard
From here a composite edge magnitude image of the two can be calculated
as shown,
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
G_{composite}=\sqrt{G_{x}^{2}+G_{y}^{2}}
\]
\end_inset
\end_layout
\begin_layout Standard
With the angles of the edges calculated as follows,
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
\Theta=\arctan\left(\frac{G_{y}}{G_{x}}\right)
\]
\end_inset
\end_layout
\begin_layout Subsubsection
Application
\end_layout
\begin_layout Standard
To create a descriptor, both the angle and magnitude information will be
used, the descriptor itself will reflect information about the angles of
the edges found.
\end_layout
\begin_layout Standard
First the image grid cells will be thresholded using the magnitude values.
Magnitude values can be seen to represent the confidence with which edges
can be found and so here a decision is effectively being made as to what
are and are not edges, this value can be tuned to best match the application.
\end_layout
\begin_layout Standard
Once a thresholded edge magnitude image has been found, a normalised histogram
will be calculated for the angles of these edges.
This histogram for each grid cell will act as the descriptor when concatenated
into a vector of dimensionality,
\begin_inset Formula $D$
\end_inset
,
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
D_{total}=W\cdotp H\cdotp q
\]
\end_inset
\end_layout
\begin_layout Standard
Where
\begin_inset Formula $q$
\end_inset
refers to the number of edge histogram bins.
\end_layout
\begin_layout Section
Principal Component Analysis
\end_layout
\begin_layout Standard
When extracting a descriptor from an image an important design factor is
keeping the dimensionality as low as appropriate.
This is so as to reduce the effect of the 'curse of dimensionality', a
set of adverse phenomena which arise when analysing high dimensional data.
Each additional dimension exponentially increases the volume of the feature
space, a higher dimensional descriptor requires a longer time to extract,
process and store.
Many of these dimensions will have little to no variation and so their
addition adds complexity without adding significant extra value.
\end_layout
\begin_layout Standard
Principal component analysis is the process of identifying the orthogonal
directions and magnitudes of variation in a dataset.
For a multidimensional descriptor this is called the covariance.
The covariance can be decomposed, or 'factorised', into matrices of eigenvector
s and values defining the directions and magnitudes of the model's variations.
This allows low variation dimensions to be remove by identifying vectors
with low eigenvalues.
\end_layout
\begin_layout Standard
This reduced model can be used to project the dataset into the same lower
dimensionality, reducing complexity but retaining the majority of variation
in the remaining dimensions.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename pca-example.png
lyxscale 30
width 70col%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
Visual depiction of principal 2D component analysis resulting in a new reference
frame
\begin_inset CommandInset label
LatexCommand label
name "fig:pca-visual-depiction"
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "setosa_pca"
literal "false"
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The process can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:pca-visual-depiction"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
The left hand side shows the data presented in the root reference frame.
The right hand side shows the same data plotted in the reference frame
defined by it's own variation, in essence 'recentering' the axes around
the data.
\end_layout
\begin_layout Section
Distance Measures
\end_layout
\begin_layout Standard
Once image descriptors are plotted in a feature space, a visual search system
makes comparisons by measuring the distance between them.
This is calculated by finding the vector between the two points in space.
\end_layout
\begin_layout Standard
In mathematics the length of a vector is evaluated using a function referred
to as a norm.
Different types of norm can affect the performance of the system and therefore
different norms should be used and compared.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename l2norm.jpg
lyxscale 30
width 30col%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
A single 2D Cartesian co-ordinate with it's component lengths in blue and
its Euclidean magnitude in red
\begin_inset CommandInset label
LatexCommand label
name "fig:norm"
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
L1 Norm
\end_layout
\begin_layout Standard
The L1 norm, or Manhattan distance, is the sum of the absolute values of
the vector.
\end_layout
\begin_layout Standard
For a 2D vector,
\begin_inset Formula $x=\left(i,j\right)$
\end_inset
, the L1 norm can be calculated by,
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
\left\Vert x\right\Vert _{1}=\left|i\right|+\left|j\right|
\]
\end_inset
\end_layout
\begin_layout Standard
The L1 norm of the point defined in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:norm"
plural "false"
caps "false"
noprefix "false"
\end_inset
can be found by summing the lengths of the blue lines.
\end_layout
\begin_layout Subsection
L2 Norm
\end_layout
\begin_layout Standard
The L2 norm, or Euclidean distance, is the shortest distance between two
points in space, it is also referred to as the magnitude of a vector.
In a three dimensional Euclidean space the magnitude of a vector,
\begin_inset Formula $x=\left(i,j,k\right)$
\end_inset
, is given by,
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
\left\Vert x\right\Vert _{2}=\sqrt{i^{2}+j^{2}+k^{2}}
\]
\end_inset
\end_layout
\begin_layout Standard
It's intuitive
\begin_inset Quotes eld
\end_inset
as the crow flies
\begin_inset Quotes erd
\end_inset
distance measurement makes it the most commonly used norm in Euclidean
space.
In figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:norm"
plural "false"
caps "false"
noprefix "false"
\end_inset
the length of the red line defines this point's Euclidean distance.
\end_layout
\begin_layout Subsection
Mahalanobis Distance
\end_layout
\begin_layout Standard
The Mahalanobis distance extends the L2 norm to better account for the nature
of a data model defined through principal component analysis.
Instead of calculating the distance between two points in a feature space,
a model represents a region in space and here a more useful distance would
that between a point and the closest part of that region in space.
\end_layout
\begin_layout Standard
The Mahalanobis distance allows this to be calculated by measuring the standard
deviations away from the model's mean a point is, in doing so normalising
for the model's shape in space.
When moving from the root frame of reference to the frame defined by a
model defined through PCA, the Mahalanobis distance becomes Euclidean distance.
In essence we are finding the distance a point is from a model from the
model's frame of reference.
\end_layout
\begin_layout Standard
The Mahalanobis distance,
\begin_inset Formula $d$
\end_inset
, can be calculated with
\begin_inset Formula
\[
d^{2}=\left(x-\mu\right)^{T}C^{-1}\left(x-\mu\right)
\]
\end_inset
\end_layout
\begin_layout Standard
When the covariance,
\begin_inset Formula $C$
\end_inset
, undergoes eigendecomposition the eigenvectors,
\begin_inset Formula $U$
\end_inset
, and eigenvalues,
\begin_inset Formula $V$
\end_inset
, can be used to calculate the Mahalanobis distance with
\begin_inset Formula
\[
d^{2}=\left|V^{-1}U^{-1}\left(x-\mu\right)\right|
\]
\end_inset
\end_layout
\begin_layout Section
Test Methods
\end_layout
\begin_layout Subsection
Dataset
\begin_inset CommandInset label
LatexCommand label
name "subsec:Dataset"
\end_inset
\end_layout
\begin_layout Standard
For the purposes of these experiments the Microsoft MSRC
\begin_inset CommandInset citation
LatexCommand cite
key "microsoft_msrc"
literal "false"
\end_inset
version 2 dataset was used.
The set is made up of 591 images across 20 categories, the classifications
for which can be seen in appendix
\begin_inset CommandInset ref
LatexCommand ref
reference "sec:MSRC-Dataset-Classifications"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\end_layout
\begin_layout Standard
Worth noting about the dataset is that there are some similarities and overlap
between categories which has implications on the results which can be calculate
d when using it.
\end_layout
\begin_layout Standard
For example category 1 is a collection of images of cows, sheep and horses
on grass however cows and sheep each have their own distinct categories.
Category 18 also has many similarities to category 20 with both being mainly
shots of bodies of water and boats in water of varying sizes.
\end_layout
\begin_layout Standard
During the evaluation of implemented visual search techniques the classification
of each image is done by referencing the group index they are named with.
As such, occurrences of false negatives may increase as images that do
in fact look similar as they are both, say, images of cows will be marked
as not the right category and measure negatively for the performance of
the method.
\end_layout
\begin_layout Subsection
Precision and Recall
\end_layout
\begin_layout Standard
When comparing the effectiveness of different descriptors the main measurements
are those of precision and recall.
\end_layout
\begin_layout Standard
Once the visual search system has ranked a dataset on similarity to a query
image, the precision and recall can be calculated up to
\begin_inset Formula $n$
\end_inset
images through the ranked list.
\end_layout
\begin_layout Standard
At each
\begin_inset Formula $n$
\end_inset
the precision is defined as the number of images up to
\begin_inset Formula $n$
\end_inset
that are classed as relevant.
Higher precision values indicate better system performance and an ideal
system response as
\begin_inset Formula $n$
\end_inset
increases would be a precision of 1 until all relevant documents have been
returned at which point it would gradually reduce to a minimum value of
the fraction of relevant documents in the dataset.
This would indicate that the system is able to select a relevant image
every time one is available.
\end_layout
\begin_layout Standard
The recall is defined at
\begin_inset Formula $n$
\end_inset
as how many of the available relevant results have been returned up to
\begin_inset Formula $n$
\end_inset
.
Higher recall values at
\begin_inset Formula $n$
\end_inset
indicate that the system can recall relevant documents faster with less
false positives and begins at 0 before increasing to a maximum of 1 as
\begin_inset Formula $n$
\end_inset
increases when all have been returned.
\end_layout
\begin_layout Standard
While both measurements appear to reflect similar concepts there is a difference.
Precision is a measure of how accurately a system can decide whether a
document is relevant while recall can be thought of as a measure of a systems
repeated accuracy and measures how long it takes to retrieve all relevant
documents.
\end_layout
\begin_layout Standard
A system with high recall but low precision at
\begin_inset Formula $n$
\end_inset
will indicate that the system is effectively able to retrieve all relevant
documents eventually however there will be false positives within the results.
Results of this quality would be advantageous when it is important to obtain
all relevant results however not when the relevance of each and every one
is valued.
\end_layout
\begin_layout Standard
A system with high precision but low recall at
\begin_inset Formula $n$
\end_inset
would indicate that the system is very confident in its selection of relevant
documents but may indicate an increase in false negatives where the system
cannot correctly recognise a target image.
\end_layout
\begin_layout Subsection
Precision Recall Curve
\end_layout
\begin_layout Standard
A way to visualise the response of a visual search system is to calculate
both precision and recall for all values of
\begin_inset Formula $n$
\end_inset
and plot each pair in a precision-recall or PR curve.
\end_layout
\begin_layout Standard
When plotted in this fashion with recall along the
\begin_inset Formula $x$
\end_inset
axis and and precision along
\begin_inset Formula $y$
\end_inset
the curve can be thought to plot the system performance over normalised
time to retrieve the query's category set.
\end_layout
\begin_layout Standard
Perfect system performance is given by,
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
y=\begin{cases}
1 & 0<x\leq1\\
0 & x>1
\end{cases}
\]
\end_inset
\end_layout
\begin_layout Subsection
Methods
\end_layout
\begin_layout Standard
Results were calculated for the global colour histogram, spatial colour,
spatial texture and a combined descriptor of spatial colour and texture.
Principal component analysis was also conducted on the spatial colour and
texture descriptor in order to identify the optimum levels of dimensionality
reduction.
Each descriptor has parameters that can be varied to alter it's performance.
For each varying parameter a category response test was conducted.
\end_layout
\begin_layout Subsubsection
Category Response
\end_layout
\begin_layout Standard
The category response test performs a number of queries on different images
in order to calculate average performance values.
These values are used to describe the performance of the applied descriptor,
in order to make comparisons between descriptors valid the same query images
are used for all investigations.
\end_layout
\begin_layout Standard
The category response aims to control for a descriptor's varying performance
at each of the dataset's classifications by selecting 1 query image from
each that was visually deemed to well represent the rest.
The query set filenames used in these tests can be seen in appendix
\begin_inset CommandInset ref
LatexCommand ref
reference "sec:Query-Set"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
Each query iteration has precision and recall calculated for all
\begin_inset Formula $n$
\end_inset
to allow the mean average precision to be calculated.
\end_layout
\begin_layout Standard
An average precision-recall curve was calculated by plotting the average
precision and recall values for the 20 category iteration results.
An example of the 20 precision-recall curves reduced to an average line
is seen in appendix
\begin_inset CommandInset ref
LatexCommand ref
reference "subsec:Global-Colour-Histogram-pr-n-4"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\end_layout
\begin_layout Standard
Completing an iteration for each category also allows a confusion matrix
to be constructed.
For each iteration the top 25 results were evaluated, this number was chosen
as it is similar to the average category size.
\end_layout
\begin_layout Standard
The completed confusion matrix allows the main category confusions to be
identified and discussions to be made, each column represents an iteration
of the category response test and the matrix has been normalised by column.
\end_layout
\begin_layout Subsubsection
Global Colour Histogram
\end_layout
\begin_layout Standard
For the global colour histogram the number of bins was varied to view the
effect of this on the MAP.
Values between 1 and 20 were investigated.
\end_layout
\begin_layout Standard
The effect was measured using L1, Euclidean and Mahalanobis distance measures
although not all bin values could be evaluated for Mahalanobis distance
due to prohibitively high processing time.
\end_layout
\begin_layout Subsubsection
Spatial Colour
\end_layout
\begin_layout Standard
For spatial colour, category response tests were completed as the grid dimension
s were varied from 1x1 to 20x20.
The resulting matrix of results were plotted as a surface to see how variations
in grid size affected mean average precision.
\end_layout
\begin_layout Standard
The average precision recall curve was plotted for the best performing parameter
s found in these investigations.
These experiments were doing using L2 (Euclidean) Distance.
\end_layout
\begin_layout Subsubsection
Spatial Texture
\end_layout
\begin_layout Standard
For spatial texture the grid dimensions were brought forward from a high
achieving spatial colour investigation in order to evaluate the effect
of varying both bin count and threshold value.
While investigating each, the other was kept the same.
Following calculations for both, the best value for each was used to test
the effect of varying grid dimensions.
Grid dimensions were not varied as widely as for spatial colour due to
the prohibitively long processing time.
\end_layout
\begin_layout Standard
The initial threshold value to be held stable during bin count investigations
was picked by visualising different threshold values, examples can be seen
in figures
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Angle-magnitude-0.01"
plural "false"
caps "false"
noprefix "false"
\end_inset
through
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Angle-magnitude-0.2"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
A value of 0.08 was selected, the visualisation can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Angle-magnitude-0.08"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
These experiments were doing using Euclidean Distance.
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/edgeThresholds/10_14_s.png
lyxscale 30
width 20col%
\end_inset
\begin_inset space \quad{}
\end_inset
\begin_inset Graphics
filename ../data/edgeThresholds/10_14_flower_edge.png
lyxscale 30
width 20col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Image 10_14_s from the flower category followed by visualisation of detected
edges
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/edgeThresholds/5_15_s.png
lyxscale 30
width 20col%
\end_inset
\begin_inset space \quad{}
\end_inset
\begin_inset Graphics
filename ../data/edgeThresholds/5_15_cow_edge.png
lyxscale 30
width 20col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Image 5_15_s from the cow category followed by visualisation of detected
edges
\begin_inset CommandInset label
LatexCommand label
name "fig:cow-pic"
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/edgeThresholds/flower-t-0.01.png
lyxscale 30
width 20col%
\end_inset
\begin_inset space \quad{}
\end_inset
\begin_inset Graphics
filename ../data/edgeThresholds/cow-t-0.01.png
lyxscale 30
width 20col%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
Angle magnitude images with threshold 0.01
\begin_inset CommandInset label
LatexCommand label
name "fig:Angle-magnitude-0.01"
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/edgeThresholds/flower-t-0.08.png
lyxscale 30
width 20col%
\end_inset
\begin_inset space \quad{}
\end_inset
\begin_inset Graphics
filename ../data/edgeThresholds/cow-t-0.08.png
lyxscale 30
width 20col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Angle magnitude images with threshold 0.08
\begin_inset CommandInset label
LatexCommand label
name "fig:Angle-magnitude-0.08"
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/edgeThresholds/flower-t-0.2.png
lyxscale 30
width 20col%
\end_inset
\begin_inset space \quad{}
\end_inset
\begin_inset Graphics
filename ../data/edgeThresholds/cow-t-0.2.png
lyxscale 30
width 20col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Angle magnitude images with threshold 0.2
\begin_inset CommandInset label
LatexCommand label
name "fig:Angle-magnitude-0.2"
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Subsubsection
Spatial Colour and Texture
\end_layout
\begin_layout Standard
The same spatial texture and grid dimension parameters were used when the
colour and texture descriptors were combined in order to allow comparison
between the results.
The grid dimensions were also varied between the same ranges.
These experiments were doing using Euclidean Distance.
\end_layout
\begin_layout Subsubsection
Principal Component Analysis
\end_layout
\begin_layout Standard
The covariance of all descriptors is calculated in order to define a model
for the dataset.
In doing so dimensions with low variation can be identified and dropped
by sorting the eigenvectors by magnitude of eigenvalue.
\end_layout
\begin_layout Standard
The Mahalanobis distance was found between each pair of images.
\end_layout
\begin_layout Standard
The proportion of eigenvalues that are dropped is referred to as the percentage
energy reduction and this energy reduction can be varied.
\end_layout
\begin_layout Standard
To visualise the effect of varying dimensionality reduction, descriptors
with previously used parameter sets were tested with varying levels of
energy reduction to view it's effect on achievable mean average precision.
\end_layout
\begin_layout Subsubsection
Descriptor Distance Measure
\end_layout
\begin_layout Standard
With well performing spatial parameters ascertained, mean average precision
results were taken for L1, L2 (Euclidean) and Mahalanobis distance measures.
These results were used to compare the performance of each distance measure
with each descriptor and also as final results to summarise the relative
performance of each descriptor.
\end_layout
\begin_layout Section
Results
\end_layout
\begin_layout Subsection
Global Colour Histogram
\end_layout
\begin_layout Standard
The effect of varying numbers of histogram bins can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:colour-histogram-bin-vary"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
L1 performed slightly better than L2 at lower bins but continues to increase
in performance as Euclidean and Mahalanobis distances peak and begin to
decline.
Euclidean and Mahalanobis distance both peak at 5 bins (0.145 and 0.152 respectiv
ely) while the L1 norm peaks at 15 with a value of 0.167.
3 bins has a noticeably lower MAP result for all distance measures, 4 bins
also seems to be slightly below the trend of increase.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/colourHistogram/map-line.png
lyxscale 30
width 80col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Global colour histogram mean average precision values for varying numbers
of bins
\begin_inset CommandInset label
LatexCommand label
name "fig:colour-histogram-bin-vary"
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The average PR curve for the best L2 mean average precision, a bin count
of 5, can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:colour-histogram-avg-pr-n=5"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/colourHistogram/pr-curves-n-5-avg.png
lyxscale 30
width 40col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Global colour histogram mean precision recall curve for
\begin_inset Formula $n=5$
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:colour-histogram-avg-pr-n=5"
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The confusion matrix for 5 bins can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:colour-histogram-confusion"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
The most confused category combinations were confusing cars for bikes and
bikes for faces.
The best classified category was book shelves.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename /home/andy/dev/matlab/cv-coursework/data/colourHistogram/cm-5.jpg
lyxscale 30
width 100col%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
Global colour histogram confusion matrix for 5 bins
\begin_inset CommandInset label
LatexCommand label
name "fig:colour-histogram-confusion"
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Spatial Colour
\end_layout
\begin_layout Standard
The surface plotted by each dimension combination's MAP can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:spatial-colour-map-surfaces"
plural "false"
caps "false"
noprefix "false"
\end_inset
, the results up to 15x10 can be seen in appendix
\begin_inset CommandInset ref
LatexCommand ref
reference "sec:spatial-colour-grid-maps"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
The MAP starts at a low value with either dimension being 1, the lowest
value being for a 1x2 grid (0.1079).
As rows increase from 1 to 2 a performance increase can be seen.
This then decreases for 3 rows before increasing to a maxima at 4.
The increase from 1 to 2 columns sees a decrease in MAP before increasing
up to 4 columns.
\end_layout
\begin_layout Standard
Past 4 in either dimension the performance tends to decrease.
The highest achieved MAP was for 14 rows and 4 columns with a value of
0.156.
The average PR curve for this result can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:spatial-colour-avg-pr"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/spatialColour/mapSurface2.png
lyxscale 20
width 50col%
\end_inset
\begin_inset Graphics
filename ../data/spatialColour/mapSurfaceWithMax.png
lyxscale 20
width 50col%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
Mean average precision values for varying dimensions of spatial colour grid,
maximum value labelled
\begin_inset CommandInset label
LatexCommand label
name "fig:spatial-colour-map-surfaces"
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/spatialColour/avg-pr-4-14.png
lyxscale 30
width 40col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Mean precision recall curve for spatial colour grid 14 rows, 4 columns
\begin_inset CommandInset label
LatexCommand label
name "fig:spatial-colour-avg-pr"
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
parameter
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Spatial Texture
\end_layout
\begin_layout Standard
A grid of 4x4 was used initially due to it's favourable results for spatial
colour.
The MAP values achieved for different numbers of histogram bins can be
seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Spatial-texture-bin-vary"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
The MAP generally increases up to 7 bins with a value of 0.210, before decreasin
g.
Odd numbers of bins tended to achieve higher MAPs than even bins up to
this maxima before linearly decreasing.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/spatialTexture/map-bin-vary.png
lyxscale 30
width 70col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Spatial texture mean average precision values for varying numbers of bins,
grid size 4x4, threshold 0.08
\begin_inset CommandInset label
LatexCommand label
name "fig:Spatial-texture-bin-vary"
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The MAP values for varying edge magnitude thresholds can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Spatial-texture-thresh-vary"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
The MAP increases as threshold increases until a threshold of 0.09 (0.214).
From here it decreases.
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/spatialTexture/map-thresh-vary.png
lyxscale 30
width 70col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Spatial texture mean average precision values for varying threshold, grid
size 4x4, 7 bins
\begin_inset CommandInset label
LatexCommand label
name "fig:Spatial-texture-thresh-vary"
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
A threshold of 0.09 and bin count of 7 was used to measure the effect of
grid dimensions on mean average precision, the results for which can be
seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:spatial-texture-map-surface"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
The highest MAP was achieved for 4 rows and 3 columns with a value of 0.231.
The lowest recorded value was 0.157 for 1 row and 6 columns.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/spatialTexture/mapSurface.png
lyxscale 20
width 50col%
\end_inset
\begin_inset Graphics
filename ../data/spatialTexture/mapSurfaceWithMax.png
lyxscale 20
width 50col%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
Mean average precision values for varying dimensions of spatial texture
grid, 7 bins, threshold 0.09
\begin_inset CommandInset label
LatexCommand label
name "fig:spatial-texture-map-surface"
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The average PR curve using the best performing of each parameter and a grid
of 4x3 can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:spatial-texture-avg-pr"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/spatialTexture/avg-pr-4-3-7-0.09.png
lyxscale 30
width 40col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Mean precision recall curve for spatial texture 4x3 grid, 7 bins, threshold
0.09
\begin_inset CommandInset label
LatexCommand label
name "fig:spatial-texture-avg-pr"
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The confusion matrix for spatial texture can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Spatial-texture-confusion-matrix"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
The largest confusion was the system selecting planes and classifying them
as water features, 60% of the top 25 were misclassified in this fashion.
The best classified categories were planes and trees.
\end_layout
\begin_layout Standard
\align center
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/spatialTexture/cm-4x3-7-0.09.jpg
lyxscale 30
width 100col%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
Spatial texture confusion matrix 4x3 grid, 7 bins, threshold 0.09
\begin_inset CommandInset label
LatexCommand label
name "fig:Spatial-texture-confusion-matrix"
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Spatial Colour and Texture
\end_layout
\begin_layout Standard
The same parameters from spatial texture (7 bins, edge magnitude threshold
0.09) were used to investigate how grid dimensions affect mean average precision
, the results for which can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:spatial-colour-texture-map-surface"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/spatialColourTexture/mapSurface.png
lyxscale 20
width 50col%
\end_inset
\begin_inset Graphics
filename ../data/spatialColourTexture/mapSurfaceWithMax.png
lyxscale 20
width 50col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Mean average precision values for varying dimensions of spatial colour and
texture grid, 7 bins, threshold 0.09
\begin_inset CommandInset label
LatexCommand label
name "fig:spatial-colour-texture-map-surface"
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
As rows increase the MAP increases up to 4 rows before tending to decrease
past here.
As columns increase from 1 to 2 the mean average precision decreases before
increasing for 3 columns and then decreasing.
The highest MAP was found at 4 rows and 3 columns with a value of 0.217.
The lowest value was found at 2 columns and 1 row with a value of 0.1489.
The average PR curve for the best performing grid dimensions can be seen
in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:spatial-colour-texture-avg-pr"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/spatialColourTexture/avg-pr-4-3-7-0.09.png
lyxscale 30
width 40col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Mean precision recall curve for spatial colour and texture, 4 rows, 3 columns,
7 bins, threshold 0.09
\begin_inset CommandInset label
LatexCommand label
name "fig:spatial-colour-texture-avg-pr"
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The confusion matrix for combined spatial colour and texture can be seen
in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:spatial-colour-texture-confusion-matrix"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
Here the largest errors are classifying sheep as birds, farm animals as
cows and signs as book shelves.
The most correctly classified images were trees and sheep.
\end_layout
\begin_layout Standard
Comparing with the spatial texture matrix, the high proportion of plane's
being classed as water features decreased from 60% to 12%.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/spatialColourTexture/cm-4x3-7-0.09.jpg
lyxscale 30
width 100col%
\end_inset
\begin_inset Caption Standard
\begin_layout Plain Layout
Confusion matrix for spatial colour and texture, 4 rows, 3 columns, 7 bins,
threshold 0.09
\begin_inset CommandInset label
LatexCommand label
name "fig:spatial-colour-texture-confusion-matrix"
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Principal Component Analysis
\end_layout
\begin_layout Standard
The spatial colour and texture parameters from before (4x3 grid, 7 bins,
0.09 threshold) were used to ascertain how varying model energy reduction
affects mean average precision, the results for which can be seen in figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:pca-colour-texture-map"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
As energy is reduced the MAP increases to a maximum between 0.5% and 4%.
Beyond this reduction the mean average precision decreases to a constant
value.
\end_layout
\begin_layout Standard
The red line indicates the mean average precision achieved with the same
descriptor parameters without PCA.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/PCA/colour-texture-4x3-7-0.09-deflation-100.png
lyxscale 30
width 60col%
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename ../data/PCA/colour-texture-4x3-7-0.09-deflation-20.png
lyxscale 30
width 60col%
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
Red line indicates MAP without PCA
\begin_inset Caption Standard
\begin_layout Plain Layout
MAP for spatial colour and texture (4x3, 7 bins, thres.
0.09) as percentage energy is reduced
\begin_inset CommandInset label
LatexCommand label
name "fig:pca-colour-texture-map"
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The summary data table of these results can be seen in table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:pca-colour-texture-map"
plural "false"
caps "false"
noprefix "false"
\end_inset
, varying resolution's of the independent variable are in order to highlight
the important values of energy reduction, namely the points at which it
exceeds the non-PCA value and the highest achieved value.
A higher resolution table of values can be seen in appendix
\begin_inset CommandInset ref
LatexCommand ref
reference "sec:pca-map"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
Red values again indicate improvements over queries without PCA.
The highest achieved MAP was 0.251 at 1.4% reduction.
This represents a 16% increase over the 0.217 value for queries without
PCA.
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0.2023
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Red values indicate improvements over query without PCA (0.217)
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Mean average precision values for spatial colour and texture (4x3, 7 bins,
thres.
0.09) as dimensionality is reduced through PCA
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name "tab:pca-colour-texture-map"
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The confusion matrix for spatial colour and texture with PCA can be seen
in figure
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LatexCommand ref
reference "fig:pca-colour-texture-confusion-matrix"
plural "false"
caps "false"
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.
The largest confusions were classifying signs as bookshelves, cat's as
the coast and sheep as birds.
The most correctly classified categories were planes, book shelves and
trees.
\end_layout
\begin_layout Standard
Looking to the confusion matrix without PCA, the already high confusion
of sign's as bookshelves has increased from 32% to 40% despite the correct
classifications also increasing from 44% to 48%.
\end_layout
\begin_layout Standard
The high confusion of sheep as bird's has decreased from 44% to 32%.
However the percentage of farm animals being classed as bird's has increased
from 12% to 28%.
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Spatial colour and texture confusion matrix with PCA, 4 rows, 3 columns,
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\begin_layout Subsection
Descriptor Distance Measurements
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Figure
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shows a comparison of mean average precision values for different descriptors
using their best achieved results.
For spatial descriptors all have a grid of 4 by 4 in order to allow a direct
comparison, spatial texture descriptors also use a bin count of 7 and edge
magnitude threshold of 0.09 in order to do the same.
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Mean average precision for different descriptors at same parameters
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The inclusion of spatial texture over solely colour derived descriptors
saw a general increase in mean average precision the outlier being Mahalanobis
distance for spatial texture which the lowest recorded value.
L1 was the best performing distance measure overall being the best for
all descriptors except spatial colour.
The use of PCA and Mahalanobis distance was the second best performing
distance in all cases except spatial texture.
This leaves Euclidean distance performing the worst of all categories except
spatial texture.
The use of L1 with a spatial colour and texture descriptor had a 53% higher
mean average precision than a global colour histogram with an L1 measure.
\end_layout
\begin_layout Section
Discussion
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\begin_layout Subsection
Global Colour Histogram
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\begin_layout Standard
All three of the distance measures increased in performance up to a peak
value.
This can be attributed to the descriptor becoming more discriminative.
Low bin counts will mean that many more pixels are placed in each bin to
the point that comparisons are less meaningful.
\end_layout
\begin_layout Standard
Equally, performance decreases past a certain point as the descriptor starts
to over-fit to the data.
Over-fitting describes a descriptor that is too sensitive to differences
in images such that it is no longer comparing meaningful variables.
\end_layout
\begin_layout Standard
For the global colour histogram if the bin size continued to increase, each
bin would eventually be comprised of only one or a handful of pixels which
cannot be compared effectively.
This explains why for bin count and many other descriptor parameters there
is an optimum value and performance doesn't just increase linearly with
that parameter.
\end_layout
\begin_layout Subsection
Spatial Colour
\end_layout
\begin_layout Standard
The lower dimension configurations of the spatial colour descriptor performing
worse can be explained the same way as low bin numbers for the global colour
histogram.
A 1x1 grid indicates that the average colour values are taken for the image
in it's entirety, this 3D descriptor would not be particularly discriminative.
\end_layout
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The significance of the best performing configuration having 14 rows is
not particularly high.
Looking to figure
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reference "fig:spatial-colour-map-surfaces"
plural "false"
caps "false"
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either dimension being 4 gives similarly good results with little variation,
the best being 14 by a slim margin.
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An interesting result was that increasing the row count from 1 to 2 increased
MAP while increasing the columns in the same fashion decreased performance.
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\begin_layout Subsection
Spatial Texture
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\begin_layout Standard
The trend of increase in performance for increasing bin counts can be attributed
to creating a more discriminative descriptor.
For example, a bin count of 1 will mean that each cell's sub-descriptor
is made up of only the count of pixels deemed to be edges without any further
information as to which direction they are facing.
As the count increases from here a maximum performance can be attained
before the descriptor starts over-fitting to the data.
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\begin_layout Standard
The interesting results of odd numbers of bins giving better than even bins
could suggest that the edges of this combination of query and dataset are
naturally more discriminative when quantised into odd numbers of bins as
opposed to even although this would require investigation to comment further.
\end_layout
\begin_layout Standard
Worth noting is that during the spacial texture experiments optimal parameters
were used as fixed variables for further testing, namely the bin count
and threshold values.
In doing so good results for the descriptor could be evaluated for this
query however the results are query and data dependent as are the rest
of the results in this coursework.
Cascading optimal parameters in further experiments like this does not
necessarily represent the best set of parameters for this descriptor as
each optimum value was found with the others fixed.
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\begin_layout Standard
This query and dataset's set of optimum parameters for a descriptor could
be experimentally found with more complex testing methods however this
is not within the scope of this work.
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\begin_layout Subsection
Spatial Colour and Texture
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Looking to figure
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this surface can be seen to be a combination of the two traced by spatial
colour and texture individually.
The spatial colour characteristic of decreasing performance from 1 to 2
columns but increasing from 1 to 2 rows is visible, but the maximum value
at 4x3 from spatial texture is also apparent.
\end_layout
\begin_layout Standard
Looking to the confusion matrices of spatial texture and spatial colour
and texture, the combinatory descriptor does not perform better in every
category.
\end_layout
\begin_layout Standard
While spatial colour and texture does, for example, reduce the mis-classificatio
n of planes as water features the percentage of correct water feature classifica
tions only increases by 4%.
Spatial colour and texture performed worse at correctly classifying
\end_layout
\begin_layout Itemize
Birds
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\begin_layout Itemize
Buildings
\end_layout
\begin_layout Itemize
Cars
\end_layout
\begin_layout Itemize
Coast
\end_layout
\begin_layout Itemize
Cows
\end_layout
\begin_layout Itemize
Human Figures
\end_layout
\begin_layout Itemize
Planes
\end_layout
\begin_layout Standard
Worth noting is the cow query classification column which shows that only
8% of the top 25 results were of the cow category.
44% were farm animals, with reference to section
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this can be explained by the visual overlap between the two categories.
Inspecting the dataset, half of the farm animals category are photos of
groups of cows which can lead to false negatives.
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Also interesting, looking to figure
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, was the slight increase in mean average precision between spatial texture
and the inclusion of colour information.
It could be expected that including both would provide greater improvements
over applying either separately than were observed.
\end_layout
\begin_layout Subsection
Principal Component Analysis
\end_layout
\begin_layout Standard
The reduction of eigenvalue energy for a model was seen to increase achievable
MAP for the spatial colour and texture descriptor when done within a specific
range.
The initial increase in performance can be attributed to removing dimensions
of low variance and importance.
In doing so the remaining descriptor can be more discriminative.
\end_layout
\begin_layout Standard
However past this point relevant information starts being removed resulting
in degradation of performance.
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\begin_layout Standard
In summary the use of PCA and Mahalanobis distance does not inherently increase
performance but using a reasonable level of energy reduction such as 2%
can increase performance.
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Comparisons
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Looking to figure
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it can be seen that spatial texture descriptors can achieve better mean
average precision values than solely colour information based descriptors.
This indicates that using spatial information can form a more discriminative
descriptor than without.
\end_layout
\begin_layout Standard
The slight performance gain from the colour histogram to spatial colour
suggests that when looking solely at colour, spatial information does not
necessarily well surpass a colour histogram.
\end_layout
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When thinking about a picture of a cow like in figure
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, the spatial colour descriptor will identify an average of green surrounding
an average of brown.
The colour histogram however still identifies both the brown and green
pixels and encodes them.
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\begin_layout Standard
While it may have been thought that spatial techniques were better as a
whole than the global colour histogram, they in fact perform comparably
indicating that they may have different applications.
\end_layout
\begin_layout Standard
Spatial texture descriptors represent the largest increase in performance.
This can be attributed to spatial texture techniques attempting to identify
and describe the shapes of objects within the image as opposed to just
the colours present.
\end_layout
\begin_layout Standard
Colour exclusive methods can be less discriminative as different objects
can generate similar descriptors by being similar colours.
This could explain the global colour histogram frequently confusing cars
and bikes as both have similar metallic colours.
Using shape information allows for discrimination between images such as
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where both are white objects on a green background.
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The act of reducing the dimensionality of most of the descriptors improved
performance however this only occurs when the optimum level of reduction
is achieved.
Looking again to figure
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only the maxima of the curve represents results that were better than could
be achieved without reduction.
Without identifying this range the performance can be worse, 2% appears
to be a reasonable range to reduce by.
When using this value for all descriptors a performance increase was generally
observed indicating that it may be worth considering in most instances.
\end_layout
\begin_layout Section
Conclusions
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To summarise, each descriptor has varying efficacy for different applications
and the context should be identified before selecting one over the other.
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Optimum internal parameters for each are data and query dependedent but
well performing values can be obtained experimentally.
\end_layout
\begin_layout Standard
Spatial texture techniques were found to perform better than exclusively
colour based methods such as a global colour histogram.
While the combination of both colour and texture information didn't provide
as much of an improvement over either separately as expected, the combination
of both colour and texture information as part of the descriptor was the
best performing while using the L1 norm.
\end_layout
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The use of appropriate amounts (approximately 2%) of dimensionality reduction
following principal component analysis provided performance increases over
the standard L2 norm, however following investigations into difference
distance measures the L1 norm tended to be best performing method.
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MSRCv2 Dataset Classifications
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\end_document