added AP, MAP

.gitignore

@@ -1 +1,2 @@
 dataset
+descriptors

cvpr_computedescriptors.m

@@ -22,6 +22,7 @@ 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';
 
 allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp']));
@@ -34,6 +35,7 @@ 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);
     save(fout,'F');
     toc

cvpr_visualsearch.m

@@ -27,6 +27,7 @@ DATASET_FOLDER = 'dataset';
 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';
 
 
@@ -58,14 +59,17 @@ end
 % get counts for each category for PR calculation
 CAT_HIST = histogram(ALLCATs).Values;
 
-%% 2) Pick an image at random to be the query
+run_total = 20;
-NIMG=size(ALLFEAT,1);           % number of images in collection
+AP_values = zeros([1, run_total]);
-queryimg=floor(rand()*NIMG);    % index of a random image
+for run=1:run_total
+    %% 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
+    %% 3) Compute the distance of image to the query
-dst=[];
+    dst=[];
-for i=1:NIMG
+    for i=1:NIMG
         candidate=ALLFEAT(i,:);
         query=ALLFEAT(queryimg,:);
 
@@ -74,22 +78,26 @@ for i=1:NIMG
         %% COMPARE FUNCTION
         thedst=compareEuclidean(query,candidate);
         dst=[dst ; [thedst i category]];
-end
+    end
-dst=sortrows(dst,1);  % sort the results
+    dst=sortrows(dst,1);  % sort the results
 
-%% 3.5) Calculate PR
+    %% 3.5) Calculate PR
-precision_values=[];
+    precision_values=zeros([1, NIMG]);
-recall_values=[];
+    recall_values=zeros([1, NIMG]);
-query_row = dst(1,:);
+
-query_category = query_row(1,3);
+    correct_at_n=zeros([1, NIMG]);
-for i=1:NIMG
+
+    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
+        if i > 1    
+            for n=1:i - 1
                 row = rows(n, :);
                 category = row(3);
 
@@ -100,23 +108,56 @@ for i=1:NIMG
                 end
 
             end
+        end
+
+        % LAST ROW
+        row = rows(i, :);
+        category = row(3);
+
+        if category == query_category
+            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,query_category);
 
         precision_values(i) = precision;
         recall_values(i) = recall;
+    end
+
+
+    %% 3.6) 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);
+    
+    AP_values(run) = average_precision;
+
+
+    %% 3.8) plot PR curve
+    figure(1)
+    plot(recall_values, precision_values);
+    hold on;
+    title('PR Curve');
+    xlabel('Recall');
+    ylabel('Precision');
+
 end
 
-% plot PR curve
+%% 3.9 Calculate MAP
-plot(recall_values, precision_values);
+AP_values
-title('PR Curve');
+MAP = mean(AP_values)
-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
@@ -131,5 +172,6 @@ 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);
+figure(2)
-% axis off;
+imgshow(outdisplay);
+axis off;

cvpr_visualsearch.m~

@@ -78,8 +78,11 @@ end
 dst=sortrows(dst,1);  % sort the results
 
 %% 3.5) Calculate PR
-precision_values=[];
+precision_values=zeros([1, NIMG]);
-recall_values=[];
+recall_values=zeros([1, NIMG]);
+
+correct_at_n=zeros([1, NIMG]);
+
 query_row = dst(1,:);
 query_category = query_row(1,3);
 for i=1:NIMG
@@ -89,8 +92,9 @@ for i=1:NIMG
     correct_results = 0;
     incorrect_results = 0;
     
-    for n=1:i
+    if i > 1    
-        row = rows(i, :);
+        for n=1:i - 1
+            row = rows(n, :);
             category = row(3);
 
             if category == query_category
@@ -100,6 +104,18 @@ for i=1:NIMG
             end
 
         end
+    end
+    
+    % LAST ROW
+    row = rows(i, :);
+    category = row(3);
+
+    if category == query_category
+        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,query_category);
@@ -108,9 +124,20 @@ for i=1:NIMG
     recall_values(i) = recall;
 end
 
-% for i=1:NIMG
+
-%     [i, " -> p: ", precision_values(i), "r: ", recall_values(i)]
+%% 3.6) calculate AP
-% end
+for i = 1:NIMG
+    precision = precision_values(i);
+    i
+end
+
+
+%% 3.8) plot PR curve
+% plot(recall_values, precision_values);
+% title('PR Curve');
+% xlabel('Recall');
+% ylabel('Precision');
+
 
 %% 4) Visualise the results
 %% These may be a little hard to see using imgshow