diff --git a/cvpr_computedescriptors.m b/cvpr_computedescriptors.m
index a0af5d6..7ba1e2c 100644
--- a/cvpr_computedescriptors.m
+++ b/cvpr_computedescriptors.m
@@ -23,10 +23,10 @@ OUT_FOLDER = 'descriptors';
%% the idea is all your descriptors are in individual folders - within
%% the folder specified as 'OUT_FOLDER'.
% OUT_SUBFOLDER='avgRGB';
-OUT_SUBFOLDER='globalRGBhisto';
+% OUT_SUBFOLDER='globalRGBhisto';
% OUT_SUBFOLDER='spatialColour';
-% DESCRIPTOR_SUBFOLDER='spatialTexture';
-% OUT_SUBFOLDER='spatialColourTexture';
+% OUT_SUBFOLDER='spatialTexture';
+OUT_SUBFOLDER='spatialColourTexture';
allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp']));
for filenum=1:length(allfiles)
@@ -39,10 +39,10 @@ for filenum=1:length(allfiles)
%% EXTRACT FUNCTION
% F=extractAvgRGB(img);
- F=extractGlobalColHist(img, 2, 1, 8, 0.05);
-% F=extractSpatialColour(img, 2, 1);
-% F=extractSpatialTexture(img, 2, 1, 8, 0.05);
-% F=extractSpatialColourTexture(img);
+% F=extractGlobalColHist(img, 2, 1, 8, 0.05);
+% F=extractSpatialColour(img, 14, 4);
+% F=extractSpatialTexture(img, 4, 4, 7, 0.09);
+ F=extractSpatialColourTexture(img, 4, 4, 7, 0.09);
save(fout,'F');
toc
end
diff --git a/cvpr_visualsearch_pca_image.m b/cvpr_visualsearch_pca_image.m
index 22f0754..e63f842 100644
--- a/cvpr_visualsearch_pca_image.m
+++ b/cvpr_visualsearch_pca_image.m
@@ -28,10 +28,10 @@ DESCRIPTOR_FOLDER = 'descriptors';
%% and within that folder, another folder to hold the descriptors
%% we are interested in working with
% DESCRIPTOR_SUBFOLDER='avgRGB';
-DESCRIPTOR_SUBFOLDER='globalRGBhisto';
+% DESCRIPTOR_SUBFOLDER='globalRGBhisto';
% DESCRIPTOR_SUBFOLDER='spatialColour';
% DESCRIPTOR_SUBFOLDER='spatialTexture';
-% DESCRIPTOR_SUBFOLDER='spatialColourTexture';
+DESCRIPTOR_SUBFOLDER='spatialColourTexture';
CATEGORIES = ["Farm Animal"
"Tree"
@@ -92,6 +92,14 @@ CAT_TOTAL = length(CAT_HIST);
NIMG=size(ALLFEAT,1); % number of images in collection
+
+
+map=[];
+deflate_energy = [0:0.001:0.3];
+for var_iter=1:size(deflate_energy, 2)
+
+descriptor_list = ALLFEAT;
+
confusion_matrix = zeros(CAT_TOTAL);
all_precision = [];
@@ -104,18 +112,18 @@ for iteration=1:CAT_TOTAL
queryimg=QUERY_INDEXES(iteration); % index of a random image
%% 3) Compute EigenModel
- E = getEigenModel(ALLFEAT);
- E = deflateEigen(E, 3);
+ E = getEigenModel(descriptor_list);
+ E = deflateEigen(E, 1-deflate_energy(var_iter));
%% 4) Project data to lower dimensionality
- ALLFEAT=ALLFEAT-repmat(E.org,size(ALLFEAT,1),1);
- ALLFEAT=((E.vct')*(ALLFEAT'))';
+ descriptor_list=descriptor_list-repmat(E.org,size(descriptor_list,1),1);
+ descriptor_list=((E.vct')*(descriptor_list'))';
%% 3) Compute the distance of image to the query
dst=[];
for i=1:NIMG
- candidate=ALLFEAT(i,:);
- query=ALLFEAT(queryimg,:);
+ candidate=descriptor_list(i,:);
+ query=descriptor_list(queryimg,:);
category=ALLCATs(i);
@@ -133,9 +141,9 @@ for iteration=1:CAT_TOTAL
query_row = dst(1,:);
query_category = query_row(1,3);
- if query_category ~= iteration
- dst
- end
+% if query_category ~= iteration
+% dst
+% end
fprintf('category was %s\n', CATEGORIES(query_category))
dst = dst(2:NIMG, :);
@@ -192,32 +200,32 @@ for iteration=1:CAT_TOTAL
%% 6) plot PR curve
- figure(1)
- plot(recall_values, precision_values,'LineWidth',1.5);
- hold on;
- title('PR Curve');
- xlabel('Recall');
- ylabel('Precision');
- xlim([0 1]);
- ylim([0 1]);
+% figure(1)
+% plot(recall_values, precision_values,'LineWidth',1.5);
+% hold on;
+% title('PR Curve');
+% xlabel('Recall');
+% ylabel('Precision');
+% xlim([0 1]);
+% ylim([0 1]);
%% 7) Visualise the results
%% These may be a little hard to see using imgshow
%% If you have access, try using imshow(outdisplay) or imagesc(outdisplay)
- SHOW=25; % Show top 25 results
- dst=dst(1:SHOW,:);
- outdisplay=[];
- for i=1:size(dst,1)
- img=imread(ALLFILES{dst(i,2)});
- img=img(1:2:end,1:2:end,:); % make image a quarter size
- img=img(1:81,:,:); % crop image to uniform size vertically (some MSVC images are different heights)
- outdisplay=[outdisplay img];
-
- %populate confusion matrix
- confusion_matrix(query_category, dst(i,3)) = confusion_matrix(query_category, dst(i,3)) + 1;
- end
+% SHOW=25; % Show top 25 results
+% dst=dst(1:SHOW,:);
+% outdisplay=[];
+% for i=1:size(dst,1)
+% img=imread(ALLFILES{dst(i,2)});
+% img=img(1:2:end,1:2:end,:); % make image a quarter size
+% img=img(1:81,:,:); % crop image to uniform size vertically (some MSVC images are different heights)
+% outdisplay=[outdisplay img];
+%
+% %populate confusion matrix
+% confusion_matrix(query_category, dst(i,3)) = confusion_matrix(query_category, dst(i,3)) + 1;
+% end
% figure(3)
% imgshow(outdisplay);
% axis off;
@@ -225,18 +233,18 @@ for iteration=1:CAT_TOTAL
end
%% Plot average PR curve
-figure(4)
-mean_precision = mean(all_precision);
-mean_recall = mean(all_recall);
-plot(mean_recall, mean_precision,'LineWidth',5);
-title('PR Curve');
-xlabel('Average Recall');
-ylabel('Average Precision');
-xlim([0 1]);
-ylim([0 1]);
+% figure(4)
+% mean_precision = mean(all_precision);
+% mean_recall = mean(all_recall);
+% plot(mean_recall, mean_precision,'LineWidth',5);
+% title('PR Curve');
+% xlabel('Average Recall');
+% ylabel('Average Precision');
+% xlim([0 1]);
+% ylim([0 1]);
% normalise confusion matrix
-norm_confusion_matrix = confusion_matrix ./ sum(confusion_matrix, 'all');
+% norm_confusion_matrix = confusion_matrix ./ sum(confusion_matrix, 'all');
%% 8 Calculate MAP
% figure(4)
@@ -247,10 +255,14 @@ norm_confusion_matrix = confusion_matrix ./ sum(confusion_matrix, 'all');
% xlim([0, 1]);
MAP = mean(AP_values)
-AP_sd = std(AP_values)
+% AP_sd = std(AP_values);
% figure(2)
% plot(1:CAT_TOTAL, AP_values);
% title('Average Precision Per Run');
% xlabel('Run');
% ylabel('Average Precision');
+
+map(var_iter) = MAP;
+
+end
diff --git a/cvpr_visualsearch_selected_image.m b/cvpr_visualsearch_selected_image.m
index 0801102..801da93 100644
--- a/cvpr_visualsearch_selected_image.m
+++ b/cvpr_visualsearch_selected_image.m
@@ -28,10 +28,10 @@ DESCRIPTOR_FOLDER = 'descriptors';
%% and within that folder, another folder to hold the descriptors
%% we are interested in working with
% DESCRIPTOR_SUBFOLDER='avgRGB';
-DESCRIPTOR_SUBFOLDER='globalRGBhisto';
+% DESCRIPTOR_SUBFOLDER='globalRGBhisto';
% DESCRIPTOR_SUBFOLDER='spatialColour';
% DESCRIPTOR_SUBFOLDER='spatialTexture';
-% DESCRIPTOR_SUBFOLDER='spatialColourTexture';
+DESCRIPTOR_SUBFOLDER='spatialColourTexture';
CATEGORIES = ["Farm Animal"
"Tree"
@@ -221,7 +221,7 @@ figure(4)
mean_precision = mean(all_precision);
mean_recall = mean(all_recall);
plot(mean_recall, mean_precision,'LineWidth',5);
-title('Global Colour Histogram Average PR (n=5)');
+title('Spatial Texture Average PR (4x4, bin=7, thresh.=0.09)');
xlabel('Average Recall');
ylabel('Average Precision');
xlim([0 1]);
diff --git a/data/PCA/colour-texture-4x4-7-0.09-deflation-100.png b/data/PCA/colour-texture-4x4-7-0.09-deflation-100.png
new file mode 100644
index 0000000..837f491
Binary files /dev/null and b/data/PCA/colour-texture-4x4-7-0.09-deflation-100.png differ
diff --git a/data/PCA/colour-texture-4x4-7-0.09-deflation-20.png b/data/PCA/colour-texture-4x4-7-0.09-deflation-20.png
new file mode 100644
index 0000000..2db82ac
Binary files /dev/null and b/data/PCA/colour-texture-4x4-7-0.09-deflation-20.png differ
diff --git a/data/colourHistogram/map-line.png b/data/colourHistogram/map-line.png
index c218b04..f559eba 100644
Binary files a/data/colourHistogram/map-line.png and b/data/colourHistogram/map-line.png differ
diff --git a/data/data.ods b/data/data.ods
index 60956a3..4bd1260 100644
Binary files a/data/data.ods and b/data/data.ods differ
diff --git a/data/spatialColour/avg-pr-4-14.png b/data/spatialColour/avg-pr-4-14.png
new file mode 100644
index 0000000..d1dcf2f
Binary files /dev/null and b/data/spatialColour/avg-pr-4-14.png differ
diff --git a/data/spatialColourTexture/mapSurface.png b/data/spatialColourTexture/mapSurface.png
new file mode 100644
index 0000000..242a2d1
Binary files /dev/null and b/data/spatialColourTexture/mapSurface.png differ
diff --git a/data/spatialColourTexture/mapSurfaceWithMax.png b/data/spatialColourTexture/mapSurfaceWithMax.png
new file mode 100644
index 0000000..fd78a47
Binary files /dev/null and b/data/spatialColourTexture/mapSurfaceWithMax.png differ
diff --git a/data/spatialTexture/avg-pr-4-4-7-0.09.png b/data/spatialTexture/avg-pr-4-4-7-0.09.png
new file mode 100644
index 0000000..feb1901
Binary files /dev/null and b/data/spatialTexture/avg-pr-4-4-7-0.09.png differ
diff --git a/data/spatialTexture/map-thresh-vary.png b/data/spatialTexture/map-thresh-vary.png
new file mode 100644
index 0000000..93b3bb5
Binary files /dev/null and b/data/spatialTexture/map-thresh-vary.png differ
diff --git a/data/spatialTexture/mapSurface.png b/data/spatialTexture/mapSurface.png
new file mode 100644
index 0000000..a365ded
Binary files /dev/null and b/data/spatialTexture/mapSurface.png differ
diff --git a/data/spatialTexture/mapSurface2.png b/data/spatialTexture/mapSurface2.png
new file mode 100644
index 0000000..ef3b54f
Binary files /dev/null and b/data/spatialTexture/mapSurface2.png differ
diff --git a/data/spatialTexture/mapSurfaceWithMax.png b/data/spatialTexture/mapSurfaceWithMax.png
new file mode 100644
index 0000000..7cd7a11
Binary files /dev/null and b/data/spatialTexture/mapSurfaceWithMax.png differ
diff --git a/parameter_iter_selected_image.m b/parameter_iter_selected_image.m
index fc040bf..ad2e6a2 100644
--- a/parameter_iter_selected_image.m
+++ b/parameter_iter_selected_image.m
@@ -25,12 +25,6 @@ DATASET_FOLDER = 'dataset';
%% Folder that holds the results...
DESCRIPTOR_FOLDER = 'descriptors';
-%% and within that folder, another folder to hold the descriptors
-%% we are interested in working with
-% DESCRIPTOR_SUBFOLDER='avgRGB';
-% DESCRIPTOR_SUBFOLDER='globalRGBhisto';
-DESCRIPTOR_SUBFOLDER='spatialColour';
-% DESCRIPTOR_SUBFOLDER='spatialColourTexture';
CATEGORIES = ["Farm Animal"
"Tree"
@@ -61,10 +55,8 @@ QUERY_INDEXES=[301 358 384 436 447 476 509 537 572 5 61 80 97 127 179 181 217 26
map = [];
-thresholded = [0.01:0.01:0.15];
-
-for b=1:15
-% for c=1:10
+for r=9:9
+ for c=1:8
%% 1) Load all the descriptors into "ALLFEAT"
%% each row of ALLFEAT is a descriptor (is an image)
@@ -72,7 +64,6 @@ for b=1:15
ALLFEAT=[];
ALLFILES=cell(1,0);
ALLCATs=[];
-ctr=1;
allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp']));
for filenum=1:length(allfiles)
@@ -89,9 +80,9 @@ for filenum=1:length(allfiles)
%% EXTRACT FUNCTION
% F=extractAvgRGB(img);
% F=extractGlobalColHist(img);
-% F = extractSpatialColour(img, b, c);
- F=extractSpatialTexture(img, 4, 4, 7, thresholded(1,b));
-% F=extractSpatialColourTexture(img, b, c, 8, 0.08);
+% F=extractSpatialColour(img, r, c);
+ F=extractSpatialTexture(img, r, c, 7, 0.09);
+% F=extractSpatialColourTexture(img, r, c, 7, 0.09);
% toc
ALLFEAT=[ALLFEAT ; F];
@@ -139,7 +130,7 @@ for iteration=1:CAT_TOTAL
% if query_category ~= iteration
% dst
% end
- fprintf('category was %s\n', CATEGORIES(query_category))
+% fprintf('category was %s\n', CATEGORIES(query_category))
dst = dst(2:NIMG, :);
@@ -209,18 +200,18 @@ for iteration=1:CAT_TOTAL
%% These may be a little hard to see using imgshow
%% If you have access, try using imshow(outdisplay) or imagesc(outdisplay)
- SHOW=25; % Show top 25 results
- dst=dst(1:SHOW,:);
- outdisplay=[];
- for i=1:size(dst,1)
-% img=imread(ALLFILES{dst(i,2)});
-% img=img(1:2:end,1:2:end,:); % make image a quarter size
-% img=img(1:81,:,:); % crop image to uniform size vertically (some MSVC images are different heights)
-% outdisplay=[outdisplay img];
-
- %populate confusion matrix
- confusion_matrix(query_category, dst(i,3)) = confusion_matrix(query_category, dst(i,3)) + 1;
- end
+% SHOW=25; % Show top 25 results
+% dst=dst(1:SHOW,:);
+% outdisplay=[];
+% for i=1:size(dst,1)
+% % img=imread(ALLFILES{dst(i,2)});
+% % img=img(1:2:end,1:2:end,:); % make image a quarter size
+% % img=img(1:81,:,:); % crop image to uniform size vertically (some MSVC images are different heights)
+% % outdisplay=[outdisplay img];
+%
+% %populate confusion matrix
+% confusion_matrix(query_category, dst(i,3)) = confusion_matrix(query_category, dst(i,3)) + 1;
+% end
% figure(3)
% imgshow(outdisplay);
% axis off;
@@ -239,7 +230,7 @@ end
% ylim([0 1]);
% normalise confusion matrix
-norm_confusion_matrix = confusion_matrix ./ sum(confusion_matrix, 'all');
+% norm_confusion_matrix = confusion_matrix ./ sum(confusion_matrix, 'all');
%% 8 Calculate MAP
% figure(4)
@@ -249,8 +240,8 @@ norm_confusion_matrix = confusion_matrix ./ sum(confusion_matrix, 'all');
% xlabel('Average Precision');
% xlim([0, 1]);
-MAP = mean(AP_values)
-AP_sd = std(AP_values)
+MAP = mean(AP_values);
+% AP_sd = std(AP_values)
% figure(2)
% plot(1:CAT_TOTAL, AP_values);
@@ -258,11 +249,11 @@ AP_sd = std(AP_values)
% xlabel('Run');
% ylabel('Average Precision');
-% fprintf('%i,%i %i', b, c, MAP);
-fprintf('%i,%i', b, MAP);
+fprintf('%i,%i %i\n', r, c, MAP);
+% fprintf('%i,%i\n', r, MAP);
-% map(b, c) = MAP;
-map(b) = MAP;
+map(r, c) = MAP;
+% map(b) = MAP;
-% end
+ end
end
\ No newline at end of file
diff --git a/report/coursework.lyx b/report/coursework.lyx
index 3af2244..a6bb405 100644
--- a/report/coursework.lyx
+++ b/report/coursework.lyx
@@ -843,7 +843,7 @@ noprefix "false"
\end_inset
- the length of the red line defines this points Euclidean distance.
+ the length of the red line defines this point's Euclidean distance.
\end_layout
\begin_layout Subsection
@@ -1043,22 +1043,70 @@ y=\begin{cases}
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 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 use to describe the performance of the applied descriptor,
+ in order to make comparisons between descriptors valid the same query images
+ are used for each.
+\end_layout
+
\begin_layout Standard
The category response aims to control for a descriptor's varying performance
- at each of the dataset's categories by looping through each category and
- using a preselected image from each as the query image.
- Each category iteration has precision and recall values calculated for
- all
+ 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.
- This mean value is calculated from the 20 category iterations for the MSRCv2
- dataset.
+ This mean value is has an
+\begin_inset Formula $n$
+\end_inset
+
+ of 20 for each category of the MSRCv2 dataset.
+\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
@@ -1073,168 +1121,84 @@ The completed confusion matrix allows the main category confusions to be
identified and discussions to be made.
\end_layout
-\begin_layout Section
-Results
-\end_layout
-
-\begin_layout Subsection
+\begin_layout Subsubsection
Global Colour Histogram
\end_layout
\begin_layout Standard
-\begin_inset Float figure
-wide false
-sideways false
-status open
-
-\begin_layout Plain Layout
-
+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 Plain Layout
-\align center
-\begin_inset Graphics
- filename ../data/colourHistogram/pr-curves-n-5-avg.png
- lyxscale 30
- width 60col%
-
-\end_inset
-
-
-\begin_inset Caption Standard
-
-\begin_layout Plain Layout
-Mean precision recall curve for histogram
-\begin_inset Formula $n=5$
-\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
-
-\end_layout
-
-\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
-Mean average precision values for varying numbers of bins
-\end_layout
-
-\end_inset
-
-
-\end_layout
-
-\begin_layout Plain Layout
-
-\end_layout
-
-\end_inset
-
-
-\end_layout
-
-\begin_layout Subsection
+\begin_layout Subsubsection
Spatial Colour
\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/spatialColour/mapSurface2.png
- lyxscale 20
- width 50col%
-
-\end_inset
-
-
-\begin_inset Graphics
- filename ../data/spatialColour/mapSurfaceWithMax.png
- lyxscale 20
- width 50col%
-
-\end_inset
-
-
+For spatial colour the grid dimensions were varied from 1x1 to 20x20.
\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:glo-col-hist-map-surfaces"
-
-\end_inset
-
-
-\end_layout
-
-\end_inset
-
-
-\end_layout
-
-\begin_layout Plain Layout
-
-\end_layout
-
-\end_inset
-
-
-\end_layout
-
-\begin_layout Subsection
+\begin_layout Subsubsection
Spatial Texture
\end_layout
\begin_layout Standard
+For spatial texture the grid dimensions were held at a performative result
+ from the spatial colour investigations in order to investigate the effect
+ of varying both bin count and threshold value.
+ While investigating each, the other was kept the same.
+ Following calculations for both, the most performative 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
+
+.
+\end_layout
+
+\begin_layout Standard
+The effect of blurring the image or not before edge detection was also investiga
+ted.
\begin_inset Float figure
wide false
sideways false
status open
-\begin_layout Plain Layout
-
-\end_layout
-
\begin_layout Plain Layout
\align center
\begin_inset Graphics
@@ -1309,10 +1273,6 @@ Image 5_15_s from the cow category followed by visualisation of detected
\end_inset
-\end_layout
-
-\begin_layout Plain Layout
-
\end_layout
\end_inset
@@ -1351,15 +1311,18 @@ status open
\begin_inset Caption Standard
\begin_layout Plain Layout
-Angle magnitude images after threshold = 0.01
-\end_layout
+Angle magnitude images with threshold 0.01
+\begin_inset CommandInset label
+LatexCommand label
+name "fig:Angle-magnitude-0.01"
\end_inset
\end_layout
-\begin_layout Plain Layout
+\end_inset
+
\end_layout
@@ -1396,7 +1359,14 @@ status open
\begin_inset Caption Standard
\begin_layout Plain Layout
-Angle magnitude images after threshold = 0.08
+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
@@ -1437,7 +1407,14 @@ status open
\begin_inset Caption Standard
\begin_layout Plain Layout
-Angle magnitude images after threshold = 0.2
+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
@@ -1448,6 +1425,221 @@ Angle magnitude images after threshold = 0.2
\end_inset
+\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
+
+.
+ The highest achieved mean average precision was 0.1446 for 5 bins, the lowest
+ was 0.0998 for 34 bins.
+ The MAP generally increases up until 5 bins before decreasing gradually
+ as the bin count increases.
+ 3 bins has a noticeably lower MAP result than those around it.
+\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 70col%
+
+\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 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 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 1x1 grid (0.1103).
+ For both dimensions an increase from 1 to 2 sees a decrease in MAP, more
+ prominantly for 2 columns.
+ MAP then increases as either dimension increases to 4 before, in general,
+ decreasing past here.
+ The highest achieved MAP was for 14 rows and 4 columns with a value of
+ 0.156.
+\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
@@ -1456,9 +1648,9 @@ status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
- filename ../data/spatialTexture/map-bin-vary.png
+ filename ../data/spatialColour/avg-pr-4-14.png
lyxscale 30
- width 80col%
+ width 40col%
\end_inset
@@ -1466,8 +1658,7 @@ status open
\begin_inset Caption Standard
\begin_layout Plain Layout
-Mean average precision values for varying numbers of bins, grid size 4x4,
- threshold 0.08
+Mean precision recall curve for spatial colour grid 14 rows, 4 columns
\end_layout
\end_inset
@@ -1482,20 +1673,752 @@ Mean average precision values for varying numbers of bins, grid size 4x4,
\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 generally
+ decreasing.
+ Odd numbers of bins generally achiveve higher MAPs than even bins up to
+ this maxima before linearlly 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 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, 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 most performative of each parameter and a
+ grid of 4x4 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-4-4-7-0.09.png
+ lyxscale 30
+ width 40col%
+
+\end_inset
+
+
+\begin_inset Caption Standard
+
+\begin_layout Plain Layout
+Mean precision recall curve for spatial texture 4x4 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 Subsection
Spatial Colour and Texture
\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 Subsection
Principal Component Analysis
\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-4x4-7-0.09-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-4x4-7-0.09-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 (4x4, 7 bins, thres.
+ 0.09) as percentage energy is reduced
+\end_layout
+
+\end_inset
+
+
+\end_layout
+
+\begin_layout Plain Layout
+
+\end_layout
+
+\end_inset
+
+
+\begin_inset Float table
+wide false
+sideways false
+status open
+
+\begin_layout Plain Layout
+\align center
+\begin_inset Tabular
+
+
+
+
+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+Percentage Energy Reduction
+\end_layout
+
+\end_inset
+ |
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+Mean Average Precision
+\end_layout
+
+\end_inset
+ |
+
+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+0
+\end_layout
+
+\end_inset
+ |
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+0.1033
+\end_layout
+
+\end_inset
+ |
+
+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+0.5
+\end_layout
+
+\end_inset
+ |
+
+\begin_inset Text
+
+\begin_layout Plain Layout
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+\end_layout
+
+\end_inset
+ |
+
+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
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+\end_layout
+
+\end_inset
+ |
+
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+
+\begin_layout Plain Layout
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+\end_layout
+
+\end_inset
+ |
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+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
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+\end_layout
+
+\end_inset
+ |
+
+\begin_inset Text
+
+\begin_layout Plain Layout
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+\end_layout
+
+\end_inset
+ |
+
+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+
+\color red
+1.2
+\end_layout
+
+\end_inset
+ |
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+
+\color red
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+\end_layout
+
+\end_inset
+ |
+
+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+
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+\end_layout
+
+\end_inset
+ |
+
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+
+\begin_layout Plain Layout
+
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+\end_layout
+
+\end_inset
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+
+\begin_inset Text
+
+\begin_layout Plain Layout
+
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+
+\end_inset
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+
+\begin_layout Plain Layout
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+\end_layout
+
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+
+\begin_layout Plain Layout
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+
+\end_inset
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+
+\begin_layout Plain Layout
+
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+0.2334
+\end_layout
+
+\end_inset
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+
+\begin_inset Text
+
+\begin_layout Plain Layout
+
+\color red
+3
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+
+\end_inset
+ |
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+
+\color red
+0.2328
+\end_layout
+
+\end_inset
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+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+
+\color red
+3.5
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+
+\end_inset
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+
+\begin_inset Text
+
+\begin_layout Plain Layout
+
+\color red
+0.2279
+\end_layout
+
+\end_inset
+ |
+
+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+
+\color red
+4
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+
+\end_inset
+ |
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+
+\color red
+0.2178
+\end_layout
+
+\end_inset
+ |
+
+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+4.1
+\end_layout
+
+\end_inset
+ |
+
+\begin_inset Text
+
+\begin_layout Plain Layout
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+\end_inset
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+
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+
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+\end_inset
+ |
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+
+
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+
+\end_layout
+
+\begin_layout Plain Layout
+\begin_inset Caption Standard
+
+\begin_layout Plain Layout
+Mean average precision values for spatial colour and texture (4x4, 7 bins,
+ thres.
+ 0.09) as dimensionality is reduced through PCA
+\end_layout
+
+\end_inset
+
+
+\end_layout
+
+\end_inset
+
+
+\end_layout
+
\begin_layout Section
Discussion
\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 performative 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.
+
+\end_layout
+
+\begin_layout Standard
+This query and dataset's set of optimum paramters for a descriptor could
+ be experimentally found with more complex testing methods however this
+ is not within the scope of this work.
+\end_layout
+
\begin_layout Section
Conclusions
\end_layout
@@ -1963,6 +2886,451 @@ Coast
\end_inset
+\end_layout
+
+\begin_layout Section
+Query Set
+\begin_inset CommandInset label
+LatexCommand label
+name "sec:Query-Set"
+
+\end_inset
+
+
+\end_layout
+
+\begin_layout Standard
+\align center
+\begin_inset Tabular
+
+
+
+
+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+Category Index
+\end_layout
+
+\end_inset
+ |
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+Query Filename
+\end_layout
+
+\end_inset
+ |
+
+
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+1
+\end_layout
+
+\end_inset
+ |
+
+\begin_inset Text
+
+\begin_layout Plain Layout
+1_10_s.bmp
+\end_layout
+
+\end_inset
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+
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\end_body
diff --git a/util/deflateEigen.m b/util/deflateEigen.m
index 58fb518..8346ab7 100644
--- a/util/deflateEigen.m
+++ b/util/deflateEigen.m
@@ -1,4 +1,15 @@
function E=deflateEigen(E, param)
-E.val=E.val(1:param);
-E.vct=E.vct(:,1:param);
+totalenergy=sum(abs(E.val));
+currentenergy=0;
+rank=0;
+for i=1:size(E.vct,2)
+ if currentenergy>(totalenergy*param)
+ break;
+ end
+ rank=rank+1;
+ currentenergy=currentenergy+E.val(i);
+end
+
+E.val=E.val(1:rank);
+E.vct=E.vct(:,1:rank);
| | | | | | | | | | | |