initial commit with random descriptors and skeleton report

.gitignore

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+dataset

cvpr_compare.m

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+function dst=cvpr_compare(F1, F2)
+
+% This function should compare F1 to F2 - i.e. compute the distance
+% between the two descriptors
+
+% For now it just returns a random number
+
+dst=rand();
+
+return;

cvpr_computedescriptors.m

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+%% EEE3032 - Computer Vision and Pattern Recognition (ee3.cvpr)
+%%
+%% cvpr_computedescriptors.m
+%% Skeleton code provided as part of the coursework assessment
+%% This code will iterate through every image in the MSRCv2 dataset
+%% and call a function 'extractRandom' to extract a descriptor from the
+%% image.  Currently that function returns just a random vector so should
+%% be changed as part of the coursework exercise.
+%%
+%% (c) John Collomosse 2010  (J.Collomosse@surrey.ac.uk)
+%% Centre for Vision Speech and Signal Processing (CVSSP)
+%% University of Surrey, United Kingdom
+
+close all;
+clear all;
+
+%% Edit the following line to the folder you unzipped the MSRCv2 dataset to
+DATASET_FOLDER = 'dataset';
+
+%% Create a folder to hold the results...
+OUT_FOLDER = 'descriptors';
+%% and within that folder, create another folder to hold these descriptors
+%% the idea is all your descriptors are in individual folders - within
+%% the folder specified as 'OUT_FOLDER'.
+OUT_SUBFOLDER='globalRGBhisto';
+
+allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp']));
+for filenum=1:length(allfiles)
+    fname=allfiles(filenum).name;
+    fprintf('Processing file %d/%d - %s\n',filenum,length(allfiles),fname);
+    tic;
+    imgfname_full=([DATASET_FOLDER,'/Images/',fname]);
+    img=double(imread(imgfname_full))./255;
+    fout=[OUT_FOLDER,'/',OUT_SUBFOLDER,'/',fname(1:end-4),'.mat'];%replace .bmp with .mat
+    F=extractRandom(img);
+    save(fout,'F');
+    toc
+end

cvpr_visualsearch.m

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+%% EEE3032 - Computer Vision and Pattern Recognition (ee3.cvpr)
+%%
+%% cvpr_visualsearch.m
+%% Skeleton code provided as part of the coursework assessment
+%%
+%% This code will load in all descriptors pre-computed (by the
+%% function cvpr_computedescriptors) from the images in the MSRCv2 dataset.
+%%
+%% It will pick a descriptor at random and compare all other descriptors to
+%% it - by calling cvpr_compare.  In doing so it will rank the images by
+%% similarity to the randomly picked descriptor.  Note that initially the
+%% function cvpr_compare returns a random number - you need to code it
+%% so that it returns the Euclidean distance or some other distance metric
+%% between the two descriptors it is passed.
+%%
+%% (c) John Collomosse 2010  (J.Collomosse@surrey.ac.uk)
+%% Centre for Vision Speech and Signal Processing (CVSSP)
+%% University of Surrey, United Kingdom
+
+close all;
+clear all;
+
+%% Edit the following line to the folder you unzipped the MSRCv2 dataset to
+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='globalRGBhisto';
+
+
+%% 1) Load all the descriptors into "ALLFEAT"
+%% each row of ALLFEAT is a descriptor (is an image)
+
+ALLFEAT=[];
+ALLFILES=cell(1,0);
+ctr=1;
+allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp']));
+for filenum=1:length(allfiles)
+    fname=allfiles(filenum).name;
+    imgfname_full=([DATASET_FOLDER,'/Images/',fname]);
+    img=double(imread(imgfname_full))./255;
+    thesefeat=[];
+    featfile=[DESCRIPTOR_FOLDER,'/',DESCRIPTOR_SUBFOLDER,'/',fname(1:end-4),'.mat'];%replace .bmp with .mat
+    load(featfile,'F');
+    ALLFILES{ctr}=imgfname_full;
+    ALLFEAT=[ALLFEAT ; F];
+    ctr=ctr+1;
+end
+
+%% 2) Pick an image at random to be the query
+NIMG=size(ALLFEAT,1);           % number of images in collection
+queryimg=floor(rand()*NIMG);    % index of a random image
+
+
+%% 3) Compute the distance of image to the query
+dst=[];
+for i=1:NIMG
+    candidate=ALLFEAT(i,:);
+    query=ALLFEAT(queryimg,:);
+    thedst=cvpr_compare(query,candidate);
+    dst=[dst ; [thedst i]];
+end
+dst=sortrows(dst,1);  % sort the results
+
+%% 4) 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=15; % Show top 15 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];
+end
+imgshow(outdisplay);
+axis off;