first run at global colour histogram

compareEuclidean.m

@@ -0,0 +1,9 @@
+function dst=compareEuclidean(F1, F2)
+
+x=F1-F2;
+x=x.^2;
+x=sum(x);
+
+dst=sqrt(x);
+
+return;

cvpr_compare_random.m

@@ -1,4 +1,4 @@
-function dst=cvpr_compare(F1, F2)
+function dst=cvpr_compare_random(F1, F2)
 
 % This function should compare F1 to F2 - i.e. compute the distance
 % between the two descriptors

cvpr_computedescriptors.m

@@ -32,7 +32,9 @@ for filenum=1:length(allfiles)
     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);
+    
+    %% EXTRACT FUNCTION
+    F=extractGlobalColHist(img);
     save(fout,'F');
     toc
 end

cvpr_visualsearch.m

@@ -35,10 +35,16 @@ DESCRIPTOR_SUBFOLDER='globalRGBhisto';
 
 ALLFEAT=[];
 ALLFILES=cell(1,0);
+ALLCATs=[];
 ctr=1;
 allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp']));
 for filenum=1:length(allfiles)
     fname=allfiles(filenum).name;
+    
+    %identify photo category for PR calculation
+    split_string = split(fname, '_');
+    ALLCATs(filenum) = str2double(split_string(1));
+    
     imgfname_full=([DATASET_FOLDER,'/Images/',fname]);
     img=double(imread(imgfname_full))./255;
     thesefeat=[];
@@ -49,6 +55,9 @@ for filenum=1:length(allfiles)
     ctr=ctr+1;
 end
 
+% get counts for each category for PR calculation
+CAT_HIST = histogram(ALLCATs).Values;
+
 %% 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
@@ -59,11 +68,56 @@ dst=[];
 for i=1:NIMG
     candidate=ALLFEAT(i,:);
     query=ALLFEAT(queryimg,:);
-    thedst=cvpr_compare(query,candidate);
+    
-    dst=[dst ; [thedst i]];
+    category=ALLCATs(i);
+    
+    %% COMPARE FUNCTION
+    thedst=compareEuclidean(query,candidate);
+    dst=[dst ; [thedst i category]];
 end
 dst=sortrows(dst,1);  % sort the results
 
+%% 3.5) Calculate PR
+precision_values=[];
+recall_values=[];
+query_row = dst(1,:);
+query_category = query_row(1,3);
+for i=1:NIMG
+    
+    rows = dst(1:i, :);
+    
+    correct_results = 0;
+    incorrect_results = 0;
+    
+    for n=1:i
+        row = rows(n, :);
+        category = row(3);
+        
+        if category == query_category
+            correct_results = correct_results + 1;
+        else
+            incorrect_results = incorrect_results + 1;
+        end
+        
+    end
+    
+    precision = correct_results / i;
+    recall = correct_results / CAT_HIST(1,query_category);
+    
+    precision_values(i) = precision;
+    recall_values(i) = recall;
+end
+
+% plot PR curve
+plot(recall_values, precision_values);
+title('PR Curve');
+xlabel('Recall');
+ylabel('Precision');
+
+% for i=1:NIMG
+%     [i, " -> p: ", precision_values(i), "r: ", recall_values(i)]
+% end
+
 %% 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)
@@ -77,5 +131,5 @@ for i=1:size(dst,1)
    img=img(1:81,:,:); % crop image to uniform size vertically (some MSVC images are different heights)
    outdisplay=[outdisplay img];
 end
-imgshow(outdisplay);
+% imgshow(outdisplay);
-axis off;
+% axis off;

cvpr_visualsearch.m~

@@ -0,0 +1,129 @@
+%% 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);
+ALLCATs=[];
+ctr=1;
+allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp']));
+for filenum=1:length(allfiles)
+    fname=allfiles(filenum).name;
+    
+    %identify photo category for PR calculation
+    split_string = split(fname, '_');
+    ALLCATs(filenum) = str2double(split_string(1));
+    
+    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
+
+% get counts for each category for PR calculation
+CAT_HIST = histogram(ALLCATs).Values;
+
+%% 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,:);
+    
+    category=ALLCATs(i);
+    
+    %% COMPARE FUNCTION
+    thedst=compareEuclidean(query,candidate);
+    dst=[dst ; [thedst i category]];
+end
+dst=sortrows(dst,1);  % sort the results
+
+%% 3.5) Calculate PR
+precision_values=[];
+recall_values=[];
+query_row = dst(1,:);
+query_category = query_row(1,3);
+for i=1:NIMG
+    
+    rows = dst(1:i, :);
+    
+    correct_results = 0;
+    incorrect_results = 0;
+    
+    for n=1:i
+        row = rows(i, :);
+        category = row(3);
+        
+        if category == query_category
+            correct_results = correct_results + 1;
+        else
+            incorrect_results = incorrect_results + 1;
+        end
+        
+    end
+    
+    precision = correct_results / i;
+    recall = correct_results / CAT_HIST(1,query_category);
+    
+    precision_values(i) = precision;
+    recall_values(i) = recall;
+end
+
+% for i=1:NIMG
+%     [i, " -> p: ", precision_values(i), "r: ", recall_values(i)]
+% end
+
+%% 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;