beginning to add results

cvpr_computedescriptors.m

@@ -22,8 +22,8 @@ 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='avgRGB';
-% OUT_SUBFOLDER='globalRGBhisto';
+% OUT_SUBFOLDER='avgRGB';
+OUT_SUBFOLDER='globalRGBhisto';
 % OUT_SUBFOLDER='spatialColour';
 % OUT_SUBFOLDER='spatialColourTexture';
 
@@ -37,9 +37,9 @@ for filenum=1:length(allfiles)
     fout=[OUT_FOLDER,'/',OUT_SUBFOLDER,'/',fname(1:end-4),'.mat'];%replace .bmp with .mat
     
     %% EXTRACT FUNCTION
-    F=extractAvgRGB(img);
-%     F=extractGlobalColHist(img);
-%     F=extractSpatialColour(img);
+%     F=extractAvgRGB(img);
+    F=extractGlobalColHist(img);
+%     F=extractSpatialColour(img, 2, 1);
 %     F=extractSpatialColourTexture(img);
     save(fout,'F');
     toc

cvpr_visualsearch.m

@@ -161,17 +161,7 @@ for run=1:run_total
 
 
     %% 5) calculate AP
-    P_rel_n = zeros([1, NIMG]);
-    for i = 1:NIMG
-        precision = precision_values(i);
-        i_result_relevant = correct_at_n(i);
-
-        P_rel_n(i) = precision * i_result_relevant;
-    end
-
-    sum_P_rel_n = sum(P_rel_n);
-    average_precision = sum_P_rel_n / CAT_HIST(1,query_category);
-    
+    average_precision = sum(precision_values .* correct_at_n) / CAT_HIST(1,run);
     AP_values(run) = average_precision;
     
 

cvpr_visualsearch_pca_image.m

@@ -28,9 +28,9 @@ 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='spatialColourTexture';
+% DESCRIPTOR_SUBFOLDER='spatialColourTexture';
 
 CATEGORIES = ["Farm Animal" 
     "Tree"
@@ -93,6 +93,9 @@ NIMG=size(ALLFEAT,1);           % number of images in collection
 
 confusion_matrix = zeros(CAT_TOTAL);
 
+all_precision = [];
+all_recall = [];
+
 AP_values = zeros([1, CAT_TOTAL]);
 for iteration=1:CAT_TOTAL
     
@@ -101,7 +104,7 @@ for iteration=1:CAT_TOTAL
     
     %% 3) Compute EigenModel
     E = getEigenModel(ALLFEAT);
-    E = deflateEigen(E, 12);
+    E = deflateEigen(E, 3);
     
     %% 4) Project data to lower dimensionality
     ALLFEAT=ALLFEAT-repmat(E.org,size(ALLFEAT,1),1);
@@ -122,10 +125,10 @@ for iteration=1:CAT_TOTAL
     dst=sortrows(dst,1);  % sort the results
 
     %% 4) Calculate PR
-    precision_values=zeros([1, NIMG]);
-    recall_values=zeros([1, NIMG]);
+    precision_values=zeros([1, NIMG-1]);
+    recall_values=zeros([1, NIMG-1]);
 
-    correct_at_n=zeros([1, NIMG]);
+    correct_at_n=zeros([1, NIMG-1]);
 
     query_row = dst(1,:);
     query_category = query_row(1,3);
@@ -134,8 +137,11 @@ for iteration=1:CAT_TOTAL
     end
     fprintf('category was %s\n', CATEGORIES(query_category))
     
+    dst = dst(2:NIMG, :);
+    
     %calculate PR for each n
-    for i=1:NIMG
+    for i=1:size(dst, 1)
+        % NIMG-1 and j iterator variable is in order to skip calculating for query image
         
         rows = dst(1:i, :);
 
@@ -168,7 +174,7 @@ for iteration=1:CAT_TOTAL
         end
 
         precision = correct_results / i;
-        recall = correct_results / CAT_HIST(1,iteration);
+        recall = correct_results / (CAT_HIST(1,iteration) - 1);
 
         precision_values(i) = precision;
         recall_values(i) = recall;
@@ -176,28 +182,23 @@ for iteration=1:CAT_TOTAL
 
 
     %% 5) calculate AP
-    P_rel_n = zeros([1, NIMG]);
-    for i = 1:NIMG
-        precision = precision_values(i);
-        i_result_relevant = correct_at_n(i);
-
-        P_rel_n(i) = precision * i_result_relevant;
-    end
-
-    sum_P_rel_n = sum(P_rel_n);
-    average_precision = sum_P_rel_n / CAT_HIST(1,iteration)
-    
+    average_precision = sum(precision_values .* correct_at_n) / CAT_HIST(1,iteration);
     AP_values(iteration) = average_precision;
     
  
+    all_precision = [all_precision ; precision_values];
+    all_recall = [all_recall ; recall_values];
+    
 
     %% 6) plot PR curve
     figure(1)
-    plot(recall_values, precision_values);
+    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
@@ -222,6 +223,17 @@ 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]);
+
 % normalise confusion matrix
 norm_confusion_matrix = confusion_matrix ./ sum(confusion_matrix, 'all');
 

cvpr_visualsearch_rand_image.m

@@ -163,17 +163,7 @@ for iteration=1:CAT_TOTAL
 
 
     %% 5) calculate AP
-    P_rel_n = zeros([1, NIMG]);
-    for i = 1:NIMG
-        precision = precision_values(i);
-        i_result_relevant = correct_at_n(i);
-
-        P_rel_n(i) = precision * i_result_relevant;
-    end
-
-    sum_P_rel_n = sum(P_rel_n);
-    average_precision = sum_P_rel_n / CAT_HIST(1,iteration)
-    
+    average_precision = sum(precision_values .* correct_at_n) / CAT_HIST(1,iteration);
     AP_values(iteration) = average_precision;
     
 

cvpr_visualsearch_selected_image.m

@@ -28,9 +28,9 @@ 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='spatialColourTexture';
+% DESCRIPTOR_SUBFOLDER='spatialColourTexture';
 
 CATEGORIES = ["Farm Animal" 
     "Tree"
@@ -93,6 +93,9 @@ NIMG=size(ALLFEAT,1);           % number of images in collection
 
 confusion_matrix = zeros(CAT_TOTAL);
 
+all_precision = [];
+all_recall = [];
+
 AP_values = zeros([1, CAT_TOTAL]);
 for iteration=1:CAT_TOTAL
     
@@ -114,10 +117,10 @@ for iteration=1:CAT_TOTAL
     dst=sortrows(dst,1);  % sort the results
 
     %% 4) Calculate PR
-    precision_values=zeros([1, NIMG]);
-    recall_values=zeros([1, NIMG]);
+    precision_values=zeros([1, NIMG-1]);
+    recall_values=zeros([1, NIMG-1]);
 
-    correct_at_n=zeros([1, NIMG]);
+    correct_at_n=zeros([1, NIMG-1]);
 
     query_row = dst(1,:);
     query_category = query_row(1,3);
@@ -126,9 +129,11 @@ for iteration=1:CAT_TOTAL
     end
     fprintf('category was %s\n', CATEGORIES(query_category))
     
+    dst = dst(2:NIMG, :);
        
     %calculate PR for each n
-    for i=1:NIMG
+    for i=1:size(dst, 1)
+        % NIMG-1 and j iterator variable is in order to skip calculating for query image
 
         rows = dst(1:i, :);
 
@@ -161,7 +166,7 @@ for iteration=1:CAT_TOTAL
         end
 
         precision = correct_results / i;
-        recall = correct_results / CAT_HIST(1,iteration);
+        recall = correct_results / (CAT_HIST(1,iteration) - 1);
 
         precision_values(i) = precision;
         recall_values(i) = recall;
@@ -169,28 +174,23 @@ for iteration=1:CAT_TOTAL
 
 
     %% 5) calculate AP
-    P_rel_n = zeros([1, NIMG]);
-    for i = 1:NIMG
-        precision = precision_values(i);
-        i_result_relevant = correct_at_n(i);
-
-        P_rel_n(i) = precision * i_result_relevant;
-    end
-
-    sum_P_rel_n = sum(P_rel_n);
-    average_precision = sum_P_rel_n / CAT_HIST(1,iteration);
-    
+    average_precision = sum(precision_values .* correct_at_n) / CAT_HIST(1,iteration);
     AP_values(iteration) = average_precision;
     
     
+    all_precision = [all_precision ; precision_values];
+    all_recall = [all_recall ; recall_values];
+    
 
     %% 6) plot PR curve
-    figure(1)
-    plot(recall_values, precision_values);
-    hold on;
-    title('PR Curve');
-    xlabel('Recall');
-    ylabel('Precision');
+%     figure(1)
+%     plot(recall_values, precision_values,'LineWidth',1.5);
+%     hold on;
+%     title('Global Colour Histogram PR (n=20)');
+%     xlabel('Recall');
+%     ylabel('Precision');
+%     xlim([0 1]);
+%     ylim([0 1]);
     
     
     %% 7) Visualise the results
@@ -215,6 +215,17 @@ 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('Global Colour Histogram Average PR (n=5)');
+xlabel('Average Recall');
+ylabel('Average Precision');
+xlim([0 1]);
+ylim([0 1]);
+
 % normalise confusion matrix
 norm_confusion_matrix = confusion_matrix ./ sum(confusion_matrix, 'all');
 

descriptor/extractGlobalColHist.m

@@ -1,6 +1,6 @@
 function F=extractGlobalColHist(img)
 
-divs = 8;
+divs = 5;
 
 qimg = floor(img .* divs);
 bin = qimg(:,:,1) * divs^2 + qimg(:,:,2) * divs^1 + qimg(:,:,3);

descriptor/extractSpatialColour.m

@@ -1,7 +1,4 @@
-function F=extractSpatialColour(img)
-
-grid_rows = 10;
-grid_columns = 10;
+function F=extractSpatialColour(img, grid_rows, grid_columns)
 
 img_size = size(img);
 img_rows = img_size(1);

report/references.bib

@@ -1 +1,3 @@
 @misc{microsoft_msrc, title={Image Understanding }, url={https://www.microsoft.com/en-us/research/project/image-understanding/}, journal={Microsoft}, publisher={Microsoft}, year={2000}, month={Jan}} 
+
+@misc{setosa_pca, title={Principal Component Analysis}, url={http://setosa.io/ev/principal-component-analysis/}, journal={Explained Visually}, publisher={Setosa}, author={Powell, Victor and Lehe, Lewis}} 

single_selected_image.m

@@ -0,0 +1,280 @@
+%% 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='avgRGB';
+% DESCRIPTOR_SUBFOLDER='globalRGBhisto';
+DESCRIPTOR_SUBFOLDER='spatialColour';
+% DESCRIPTOR_SUBFOLDER='spatialColourTexture';
+
+CATEGORIES = ["Farm Animal" 
+    "Tree"
+    "Building"
+    "Plane"
+    "Cow"
+    "Face"
+    "Car"
+    "Bike"
+    "Sheep"
+    "Flower"
+    "Sign"
+    "Bird"
+    "Book Shelf"
+    "Bench"
+    "Cat"
+    "Dog"
+    "Road"
+    "Water Features"
+    "Human Figures"
+    "Coast"
+    ];
+
+QUERY_INDEXES=[301 358 384 436 447 476 509 537 572 5 61 80 97 127 179 181 217 266 276 333];
+
+% 1_10 2_16 3_12 4_4 5_15 6_14 7_17 8_15 9_1 10_14 11_8 12_26 13_10 14_10
+% 15_8 16_10 17_16 18_5 19_15 20_12
+
+map = [];
+
+for b=1:3
+    for c=1:3
+
+%% 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
+
+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;
+    
+    %identify photo category for PR calculation
+    split_string = split(fname, '_');
+    ALLCATs(filenum) = str2double(split_string(1));
+    
+    %% EXTRACT FUNCTION
+%     F=extractAvgRGB(img);
+%     F=extractGlobalColHist(img);
+    F = extractSpatialColour(img, b, c);
+%     F=extractSpatialColourTexture(img);
+%     toc
+    
+    ALLFEAT=[ALLFEAT ; F];
+end
+    
+% get counts for each category for PR calculation
+CAT_HIST = histogram(ALLCATs).Values;
+CAT_TOTAL = length(CAT_HIST);
+
+NIMG=size(ALLFEAT,1);           % number of images in collection
+
+confusion_matrix = zeros(CAT_TOTAL);
+
+all_precision = [];
+all_recall = [];
+
+AP_values = zeros([1, CAT_TOTAL]);
+for iteration=1:CAT_TOTAL
+    
+    %% 2) Pick an image at random to be the query
+    queryimg=QUERY_INDEXES(iteration);    % 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
+
+    %% 4) Calculate PR
+    precision_values=zeros([1, NIMG-1]);
+    recall_values=zeros([1, NIMG-1]);
+
+    correct_at_n=zeros([1, NIMG-1]);
+
+    query_row = dst(1,:);
+    query_category = query_row(1,3);
+    if query_category ~= iteration
+        dst
+    end
+    fprintf('category was %s\n', CATEGORIES(query_category))
+    
+    dst = dst(2:NIMG, :);
+       
+    %calculate PR for each n
+    for i=1:size(dst, 1)
+        % NIMG-1 and j iterator variable is in order to skip calculating for query image
+
+        rows = dst(1:i, :);
+
+        correct_results = 0;
+        incorrect_results = 0;
+
+        if i > 1   
+            for n=1:i - 1
+                row = rows(n, :);
+                category = row(3);
+
+                if category == iteration
+                    correct_results = correct_results + 1;
+                else
+                    incorrect_results = incorrect_results + 1;
+                end
+
+            end
+        end
+
+        % LAST ROW
+        row = rows(i, :);
+        category = row(3);
+
+        if category == iteration
+            correct_results = correct_results + 1;
+            correct_at_n(i) = 1;
+        else
+            incorrect_results = incorrect_results + 1;
+        end
+
+        precision = correct_results / i;
+        recall = correct_results / (CAT_HIST(1,iteration) - 1);
+
+        precision_values(i) = precision;
+        recall_values(i) = recall;
+    end
+
+
+    %% 5) calculate AP
+    average_precision = sum(precision_values .* correct_at_n) / CAT_HIST(1,iteration);
+    AP_values(iteration) = average_precision;
+    
+    
+    all_precision = [all_precision ; precision_values];
+    all_recall = [all_recall ; recall_values];
+    
+
+    %% 6) plot PR curve
+%     figure(1)
+%     plot(recall_values, precision_values,'LineWidth',1.5);
+%     hold on;
+%     title('Global Colour Histogram PR (n=20)');
+%     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
+%     figure(3)
+%     imgshow(outdisplay);
+%     axis off;
+
+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('Global Colour Histogram Average PR (n=20)');
+% xlabel('Average Recall');
+% ylabel('Average Precision');
+% xlim([0 1]);
+% ylim([0 1]);
+
+% normalise confusion matrix
+norm_confusion_matrix = confusion_matrix ./ sum(confusion_matrix, 'all');
+
+%% 8 Calculate MAP
+% figure(4)
+% histogram(AP_values);
+% title('Average Precision Distribution');
+% ylabel('Count');
+% xlabel('Average Precision');
+% xlim([0, 1]);
+
+MAP = mean(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');
+
+fprintf('%i,%i %i', b, c, MAP);
+
+map(b, c) = MAP;
+
+    end
+end