added edge extraction, added spatial colour texture descriptor

cvpr_computedescriptors.m

@@ -23,7 +23,9 @@ OUT_FOLDER = '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='globalRGBhisto';
+% OUT_SUBFOLDER='spatialColour';
+OUT_SUBFOLDER='spatialColourTexture';
 
 allfiles=dir (fullfile([DATASET_FOLDER,'/Images/*.bmp']));
 for filenum=1:length(allfiles)
@@ -36,7 +38,9 @@ for filenum=1:length(allfiles)
     
     %% EXTRACT FUNCTION
 %     F=extractAvgRGB(img);
-    F=extractGlobalColHist(img);
+%     F=extractGlobalColHist(img);
+%     F=extractSpatialColour(img);
+    F=extractSpatialColourTexture(img);
     save(fout,'F');
     toc
 end

cvpr_visualsearch.m

@@ -28,7 +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';
 
 
 %% 1) Load all the descriptors into "ALLFEAT"
@@ -59,7 +61,7 @@ end
 % get counts for each category for PR calculation
 CAT_HIST = histogram(ALLCATs).Values;
 
-run_total = 20;
+run_total = 100;
 AP_values = zeros([1, run_total]);
 for run=1:run_total
     %% 2) Pick an image at random to be the query
@@ -81,7 +83,7 @@ for run=1:run_total
     end
     dst=sortrows(dst,1);  % sort the results
 
-    %% 3.5) Calculate PR
+    %% 4) Calculate PR
     precision_values=zeros([1, NIMG]);
     recall_values=zeros([1, NIMG]);
 
@@ -89,6 +91,8 @@ for run=1:run_total
 
     query_row = dst(1,:);
     query_category = query_row(1,3);
+    
+    %calculate PR for each n
     for i=1:NIMG
 
         rows = dst(1:i, :);
@@ -129,7 +133,7 @@ for run=1:run_total
     end
 
 
-    %% 3.6) calculate AP
+    %% 5) calculate AP
     P_rel_n = zeros([1, NIMG]);
     for i = 1:NIMG
         precision = precision_values(i);
@@ -142,9 +146,10 @@ for run=1:run_total
     average_precision = sum_P_rel_n / CAT_HIST(1,query_category);
     
     AP_values(run) = average_precision;
+    
 
 
-    %% 3.8) plot PR curve
+    %% 6) plot PR curve
     figure(1)
     plot(recall_values, precision_values);
     hold on;
@@ -154,12 +159,18 @@ for run=1:run_total
 
 end
 
-%% 3.9 Calculate MAP
+%% 7 Calculate MAP
 AP_values
 MAP = mean(AP_values)
 
+figure(2)
+plot(1:run_total, AP_values);
+title('Average Precision Per Run');
+xlabel('Run');
+ylabel('Average Precision');
 
-%% 4) Visualise the results
+
+%% 8) Visualise the results
 %% These may be a little hard to see using imgshow
 %% If you have access, try using imshow(outdisplay) or imagesc(outdisplay)
 
@@ -172,6 +183,6 @@ 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
-figure(2)
+figure(3)
 imgshow(outdisplay);
 axis off;

descriptor/extractSpatialColour.m

@@ -0,0 +1,51 @@
+function F=extractSpatialColour(img)
+
+grid_rows = 10;
+grid_columns = 10;
+
+img_size = size(img);
+img_rows = img_size(1);
+img_cols = img_size(2);
+
+row_divs = [];
+for i = 1:grid_rows
+    row_divs(i) = i/grid_rows;
+end
+col_divs = [];
+for i = 1:grid_columns
+    col_divs(i) = i/grid_columns;
+end
+
+descriptor = [];
+%% divide image into sectors as defined grid parameters
+for i = 1:grid_rows
+    for j = 1:grid_columns
+        
+        % cell row pixel range
+        row_start = round( (i-1)*img_rows/grid_rows );
+        if row_start == 0
+            row_start = 1;
+        end
+        row_end = round( i*img_rows/grid_rows );
+        
+        % cell column pixel range
+        col_start = round( (j-1)*img_cols/grid_columns );
+        if col_start == 0
+            col_start = 1;
+        end
+        col_end = round( j*img_cols/grid_columns );
+        
+        % grab cell from parameters as above
+        img_cell = img(row_start:row_end, col_start:col_end, :);
+        
+        %take average values
+        avg_vals = extractAvgRGB(img_cell);
+        
+        %concatenate average values into vector
+        descriptor = [descriptor avg_vals(1) avg_vals(2) avg_vals(3)];
+        
+    end
+end
+
+F=descriptor;
+return;

descriptor/extractSpatialColourTexture.m

@@ -0,0 +1,55 @@
+function F=extractSpatialColourTexture(img)
+
+grid_rows = 10;
+grid_columns = 10;
+
+img_size = size(img);
+img_rows = img_size(1);
+img_cols = img_size(2);
+
+row_divs = [];
+for i = 1:grid_rows
+    row_divs(i) = i/grid_rows;
+end
+col_divs = [];
+for i = 1:grid_columns
+    col_divs(i) = i/grid_columns;
+end
+
+descriptor = [];
+%% divide image into sectors as defined grid parameters
+for i = 1:grid_rows
+    for j = 1:grid_columns
+        
+        % cell row pixel range
+        row_start = round( (i-1)*img_rows/grid_rows );
+        if row_start == 0
+            row_start = 1;
+        end
+        row_end = round( i*img_rows/grid_rows );
+        
+        % cell column pixel range
+        col_start = round( (j-1)*img_cols/grid_columns );
+        if col_start == 0
+            col_start = 1;
+        end
+        col_end = round( j*img_cols/grid_columns );
+        
+        % grab cell from parameters as above
+        img_cell = img(row_start:row_end, col_start:col_end, :);
+        grey_img_cell = getGreyscale(img_cell);
+        
+        %take average values
+        avg_vals = extractAvgRGB(img_cell);
+        [mag_img, angle_img] = getEdgeInfo(grey_img_cell);
+        
+        edge_hist = getEdgeAngleHist(mag_img, angle_img);
+        
+        %concatenate average values into vector
+        descriptor = [descriptor edge_hist avg_vals(1) avg_vals(2) avg_vals(3)];
+        
+    end
+end
+
+F=descriptor;
+return;

distance/compareL1.m

@@ -0,0 +1,6 @@
+function dst=compareL1(F1, F2)
+
+x=F1-F2;
+dst=sum(x);
+
+return;

scratch.m

@@ -1,8 +1,10 @@
 
 
 img = double(imread('dataset/Images/10_10_s.bmp'))./255;
-imshow(img);
+% imshow(img);
 
+img = getGreyscale(img);
 
-glo = extractGlobalColHist(img);
-size(img);
+[mag, angle] = getEdgeInfo(img);
+
+F = getEdgeAngleHist(mag, angle)

util/getEdgeAngleHist.m

@@ -0,0 +1,25 @@
+function F=getEdgeAngleHist(mag_img, angle_img)
+
+bins = 8;
+threshold = 0.1;
+
+dimensions = size(angle_img);
+rows = dimensions(1);
+columns = dimensions(2);
+
+vals = [];
+for i = 1:rows
+    for j = 1:columns
+        if mag_img(i, j) > threshold
+            
+            bin_value = angle_img(i, j) / (2 * pi);
+            bin_value = bin_value * bins;
+            
+            vals = [vals bin_value];
+            
+        end
+    end
+end
+
+F= histogram(vals, bins, 'Normalization', 'probability').Values;
+return;

util/getEdgeInfo.m

@@ -0,0 +1,18 @@
+function [mag_img, angle_img]=getEdgeInfo(img)
+
+blur = [1 1 1 ; 1 1 1 ; 1 1 1] ./ 9;
+blurredimg = conv2(img, blur, 'same');
+
+Kx = [1 2 1 ; 0 0 0 ; -1 -2 -1] ./ 4;
+Ky = Kx';
+
+dx = conv2(blurredimg, Kx, 'same');
+dy = conv2(blurredimg, Ky, 'same');
+
+mag_img = sqrt(dx.^2 + dy.^2);
+angle_img = atan2(dy,dx);
+
+% normalise between 0 and 2pi
+angle_img = angle_img - min(reshape(angle_img, 1, []));
+
+return;

util/getGreyscale.m

@@ -0,0 +1,5 @@
+function gryimg=getGreyscale(img)
+
+gryimg = img(:,:,1) * 0.3 + img(:,:,2) * 0.59 + img(:,:,3) * 0.11;
+
+return;