33 lines
1.7 KiB
Markdown
33 lines
1.7 KiB
Markdown
Visual Search Coursework
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Investigation into different methods of visual search including colour histograms, spatial colour, spatial texture and a combination of the two using MATLAB and the MSRCv2 dataset.
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A copy of the report can be seen [here](report.pdf). Submitted as part of the third year EE3032 Computer Vision & Pattern Recognition module, this piece achieved full marks.
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![Mean average precision comparisons for different distance measures](data/MAPComparison.png)
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![Spatial colour mean average precision for different grid dimensions](data/spatialColour/mapSurface2.png)
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![Detected edges in one of the dataset images](data/edgeThresholds/cow-t-0.08.png)
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## Code
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/data - pulled images and spreadsheet of data
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/descriptor - functions for extracting descriptors
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/distance - functions for measuring distance between descriptors
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/util - util functions such as toGreyscale and EVD
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There are two types of script, ones that run a category response once (cvpr_visualsearch_*) and ones that iteratively
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generate new descriptors to run queries on (parameter_*)
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_query_set operates using either L1 or L2 norm on the query set
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_pca generates an eigenmodel from the descriptors and computes mahalanobis distance
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_rand_image picks a random query image from each category to iterate over, no results from this script are in the paper
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The cvpr_visualsearch_* scripts load descriptors from folders and perform a category response test on them.
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The parameter_* scripts were used to generate iterative parameter results for descriptors.
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Effectively the query code from the cvpr_visualsearch_* files have been prefaced with descriptor generators that as a whole
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iterate over parameters instead of loading them from files.
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