CBIR_Query.m

% sahdy nikooei

function [feature_q] = CBIR_Query (qury_folder)
% this a function can extract features of query image
% input is the directory address from query
% output is a feature vector
  
    imageFile = dir(fullfile(qury_folder,'*.jpg')); % sort file in folder


    % preallocation (zero matrix) for save features
    features = zeros(9,16);
    
    img = imread(fullfile(qury_folder, imageFile.name));
    img = rgb2gray(img);
    
    % divide image to 16 parts for compute features 
    [rows, colns, ~] = size(img);
    block_rows = floor(rows/4);
    block_colns = floor(colns/4);
    
    for i=0:3
       for j=0:3
    
           % set block of each part
           start_row = i*block_rows+1;
           stop_row = (i+1)*block_rows;
           start_cln = j*block_colns+1;
           stop_cln = (j+1)*block_colns;
    
           part_img = img(start_row:stop_row,start_cln:stop_cln);
           
           % find column
           part_num = i*4 + j + 1;
           
           % extract features
           features(1,part_num) = mean2(part_img); % mean2 -> row:1
           features(2,part_num)= std2(part_img);  % std2 -> row:2
           features(3,part_num) = min(part_img(:)); % minIntensity -> row:3
           features(4,part_num) = max(part_img(:)); % maxIntensity -> row:4
           features(5,part_num) = sum(part_img(:)); % area -> row:5
           

           % entropy (normalize block to [0,1])
           features(6,part_num) = entropy(mat2gray(part_img)); % entropy -> row:6
           
           % energy: sum of squares of intensities
           features(7,part_num) = sum(double(part_img(:)).^2); % energy -> row:7

           % morphological operations
           part_img = imbinarize(part_img);
           part_img = ~part_img;
           part_img = imfill(part_img,"holes");
           part_img = bwareaopen(part_img,10);
           se = strel('disk', 10);
           part_img = imclose(part_img,se);
           
            % Region properties: centroid
           states = regionprops(part_img,'Centroid');
           if ~isempty(states)
               allCentroids = cat(1, states.Centroid); % Collect all centroids
               x = mean(allCentroids(:,1));            % Average X of all found centers 
               y = mean(allCentroids(:,2));            % Average Y of all found centers
           else
               x = 0;
               y = 0;
           end
           features(8,part_num) = x; % Centroid X -> row:8
           features(9,part_num) = y; % Centroid Y -> row:9
       end
    end

    feature_q = features;