3rd year computer vision coursework - visual search system and report. Achieved 100%
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/data - pulled images and spreadsheet of data
/descriptor - functions for extracting descriptors
/distance - functions fo measuring distance between descriptors
/util - util functions such as toGreyscale and EVD

There are two types of script, ones that run a category response once (cvpr_visualsearch_*) and ones that iteratively 
generate new descriptors to run queries on (parameter_*)

_query_set operates using either L1 or L2 norm on the query set
_pca generates an eigenmodel from the descriptors and computes mahalanobis distance
_rand_image picks a random query image from each category to iterate over, no results from this script are in the paper


The cvpr_visualsearch_* scripts load descriptors from folders and perform a category response test on them.

The parameter_* scripts were used to generate iterative parameter results for descriptors.
Effectively the query code from the cvpr_visualsearch_* files have been prefaced with descriptor generators that as a whole
iterate over parameters instead of loading them from files.