图像检索的matlab程序

function CBIR_Query_minmax(q_imgid, dist_id, return_no) % EE6850 HW3, Content-Based Image Retrieval % CBIR_Query() --- main script % take query, compute distance, rank them, display result % input: % query image ID (1~245) % distance metric you want to use (1x2 vector) default histogram intersection % # of output images (default 10) % output: % display % 10-19-2001, xlx@ee.columbia.edu % some global constants VERBOSE = 1; imgdir = 'H:\courses\vis\hw3\images\'; fetdir = 'H:\courses\vis\hw3\features\'; imgno = 1:245; imgsuffix = '.png'; % parse input if nargin < 1 q_imgid=input('input query image id (1~242):'); end if nargin < 2 | isempty(dist_id) dist_id = [3 3]; end if nargin < 3 return_no = 10; end % load query feature q_feature = load([fetdir,num2str(q_imgid),'.mat']); q_feature = q_feature.feature; % read features in the database, calculate the distance dist = zeros(2, length(imgno)); for i=imgno load([fetdir,num2str(i),'.mat']); % "feature": the structure containing features in the database % get distance measure here if dist_id(1) == 1 dist(1,i) = dist(1,i) + CBIR_L2dist(q_feature.colorhist, feature.colorhist); elseif dist_id(1) == 2 dist(1,i) = dist(1,i) + CBIR_cosinedist(q_feature.colorhist, feature.colorhist); elseif dist_id(1) == 3 dist(1,i) = dist(1,i) + CBIR_histintersection(q_feature.colorhist, feature.colorhist); end if dist_id(2) == 1 dist(2,i) = dist(2,i) + CBIR_L2dist(q_feature.edgedirection, feature.edgedirection); elseif dist_id(2) == 2 dist(2,i) = dist(2,i) + CBIR_cosinedist(q_feature.edgedirection, feature.edgedirection); elseif dist_id(2) == 3 dist(2,i) = dist(2,i) + CBIR_histintersection(q_feature.edgedirection, feature.edgedirection); end % ... end % combine two distance measures %dist(1,:) = dist(1,:)/std(dist(1,:)); %dist(2,:) = dist(2,:)/std(dist(2,:)); % weights of different features %lamda = 0 : 0.1 : 1; lamda = [0 1]; for i = 1:length(lamda) w = [lamda(i) 1-lamda(i)]; c_dist(i,:) = w*dist; % sort the distance list [d(i,:), index(i,:)] = sort(c_dist(i,:)); rank(i,index(i,:)) = 1:length(c_dist); %rank = index; end % then sort the rank list, pick up the "consistently close" ones c_rank = max(c_dist); [score(1,:), score(2,:)] = sort(c_rank); % pick up the top N=CxR images and display in C-columns and R-rows R = 5; C = ceil(return_no/R); figure for i = 1 : return_no img = imread([imgdir,num2str(score(2,i)),imgsuffix]); subplot(C, R, i); imshow(img); title(['No.', num2str(score(2,i)), ' d=', num2str( round(score(1,i)*1e4)*0.0001 )]); end