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visualize_retrieval_demo.m
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visualize_retrieval_demo.m
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% Version control:
% V1.5 2014/09/28
% V1.4 2014/09/16
% V1.3 2014/08/21
% V1.2 2014/08/16
% V1.1 2013/09/26
% V1.0 2013/07/22
% Author:
% yongyuan.name
close all; clear all; clc;
addpath('./utils/');
%db_name = 'CIFAR10';
%db_name = 'CALTECH256';
db_name = 'CALTECH256CNN';
param.pos = [0:1000:10000];
loopnbits = [64];
% for CALTECH256, ID=10,14, 16=airplane
query_ID = 25; % 19 21 query_ID ranges from 1 to 1000 in cifar10 (8 retrieves horse, 13 retrieves car, 15 horses)
param.numRetrieval = 25; % Number of returned retrieval images
param.query_ID = query_ID;
param.choice = 'visualization';
%hashmethods = {'Our Method'};
hashmethods = {'Our Method', 'SELVE', 'LSH', 'SH', 'SKLSH', 'DSH', 'SpH', 'CBE-opt', 'PCAH'};
%hashmethods = {'PCA-ITQ', 'PCA-RR', 'DSH', 'LSH', 'SKLSH', 'SH', 'PCAH'};
nhmethods = length(hashmethods);
% load dataset
cons_data_name = ['pre_' db_name '.mat'];
switch(cons_data_name)
case 'pre_CIFAR10.mat'
if ~exist(cons_data_name, 'file')
load cifar_10yunchao.mat;
db_datalabel = cifar10;
db_data = db_datalabel(:, 1:end-1);
exp_data = construct_data(db_name, db_data, param);
else
load pre_CIFAR10.mat;
end
clear db_data db_datalabel cifar10;
case 'pre_CALTECH256.mat'
if ~exist(cons_data_name, 'file')
load Caltech256Feature/gist.mat;
db_datalabel = feature_dataset;
db_data = normalize1(db_datalabel(:, 1:end));
clear db_datalabel;
load Caltech256Feature/gabor.mat;
db_datalabel = feature_dataset;
db_data = [db_data normalize1(db_datalabel(:, 1:end))];
exp_data = construct_data(db_name, db_data, param);
else
load pre_CALTECH256.mat;
end
clear db_data db_datalabel;
case 'pre_CALTECH256CNN.mat'
if ~exist(cons_data_name, 'file')
load 256CNN1024dNorml.mat;
db_datalabel = feat;
db_data = db_datalabel(:, 1:end);
exp_data = construct_data(db_name, double(db_data), param);
else
load pre_CALTECH256CNN.mat;
end
clear db_data db_datalabel;
end
for i =1:length(loopnbits)
fprintf('======start %d bits encoding======\n\n', loopnbits(i));
param.nbits = loopnbits(i);
for j = 1:nhmethods
[~, ~, ~, ~, ~, retrieval_list{i, j}] = demo(exp_data, param, hashmethods{1, j});
end
end
switch(db_name)
case 'CIFAR10'
% show retrieval images
load cifar-10-batches-mat/data_batch_1.mat;
data1=data;
labels1=labels;
clear data labels;
load cifar-10-batches-mat/data_batch_2.mat;
data2=data;
labels2=labels;
clear data labels;
load cifar-10-batches-mat/data_batch_3.mat;
data3=data;
labels3=labels;
clear data labels;
load cifar-10-batches-mat/data_batch_4.mat;
data4=data;
labels4=labels;
clear data labels;
load cifar-10-batches-mat/data_batch_5.mat;
data5=data;
labels5=labels;
clear data labels;
load cifar-10-batches-mat/test_batch.mat;
data6=data;
labels6=labels;
clear data labels;
database=[data1 labels1; data2 labels2; data3 labels3; data4 labels4; data5 labels5; data6 labels6];
cifar10labels=[labels1; labels2; labels3;labels4; labels5; labels6];
figure('Color', [1 1 1]); hold on;
for j = 1: nhmethods
I2 = uint8(zeros(32, 32, 3, 26)); % 32 and 32 are the size of the output image
for i=1:(param.numRetrieval+1)
index=retrieval_list{1, j}(i,1);
image_r=database(index,1:1024);
image_g=database(index,1025:2048);
image_b=database(index, 2049:end-1);
image_rer=reshape(image_r, 32, 32);
image_reg=reshape(image_g, 32, 32);
image_reb=reshape(image_b, 32, 32);
image(:, :,1)=image_rer';
image(:, :, 2)=image_reg';
image(:, :, 3)=image_reb';
image=uint8(image);
I2(:, :, :, i) = image;
end
h =subplot(2, nhmethods, j);
queryIm = I2(:, :, :, 1);
imshow(queryIm);
t = title('Query image');
p = get(t,'Position');
set(t,'Position',[p(1) p(2)+0.3 p(3)])
clear t p;
axis equal;
p = get(h, 'pos');
p(1) = p(1)-0.014 ;
p(2) = p(2)-0.05 ;
p(3) = p(3)+0.01 ;
p(4) = p(4)-0.1 ;
set(h, 'pos', p);
clear h p;
h = subplot(2, nhmethods, j+nhmethods);
p = get(h, 'pos');
p(1) = p(1)-0.024 ;
p(3) = p(3)+0.024 ;
p(4) = p(4)+0.4 ;
set(h, 'pos', p);
clear p h;
montage(I2(:, :, :, 2:param.numRetrieval+1));
title(hashmethods{j});
end
case 'CALTECH256CNN'
load 256CNN1024dNorml.mat;
allNames = rgbImgList';
%allmgs = dir('256_ObjectCategories');
%allNames = {allmgs(~[allmgs.isdir]).name};
%figure('Color', [1 1 1]); hold on;
for j = 1: nhmethods
I2 = uint8(zeros(100, 103, 3, 26)); % 32 and 32 are the size of the output image
for i=1:(param.numRetrieval+1)
index = retrieval_list{1, j}(i,1);
imName_path=['J:\Ô¬ÓÂ\E\database\256_ObjectCategories\', allNames{1, index}];
%imName_path=['256_ObjectCategories/', allNames{1, index}];
im = imread(imName_path);
im = imresize(im, [100 100]);
if (ndims(im)~=3)
I2(1:100, 1:100, 1, i) = im;
I2(1:100, 1:100, 2, i) = im;
I2(1:100, 1:100, 3, i) = im;
else
I2(1:100, 1:100, :, i) = im;
end
end
% show form 1
figure('Color', [1 1 1]);
queryIm = I2(1:100, 1:100, :, 1);
imshow(queryIm);
title('Query image');
figure('Color', [1 1 1]);
subplot(5,1,1)
montage(I2(:, :, :, 2:6), 'Size', [1 NaN]);
title(hashmethods{j});
subplot(5,1,2)
montage(I2(:, :, :, 7:11), 'Size', [1 NaN]);
subplot(5,1,3)
montage(I2(:, :, :, 12:16), 'Size', [1 NaN]);
subplot(5,1,4)
montage(I2(:, :, :, 17:21), 'Size', [1 NaN]);
subplot(5,1,5)
montage(I2(:, :, :, 22:param.numRetrieval+1), 'Size', [1 NaN]);
% show form 2
%h =subplot(2, nhmethods, j);
%queryIm = I2(:, :, :, 1);
%imshow(queryIm);
%t = title('Query image');
%p = get(t,'Position');
%set(t,'Position',[p(1) p(2)+0.3 p(3)])
%clear t p;
%axis equal;
%p = get(h, 'pos');
%p(1) = p(1)-0.014 ;
%p(2) = p(2)-0.05 ;
%p(3) = p(3)+0.01 ;
%p(4) = p(4)-0.1 ;
%set(h, 'pos', p);
%clear h p;
%h = subplot(2, nhmethods, j+nhmethods);
%p = get(h, 'pos');
%p(1) = p(1)-0.024 ;
%p(3) = p(3)+0.024 ;
%p(4) = p(4)+0.4 ;
%set(h, 'pos', p);
%clear p h;
%montage(I2(:, :, :, 2:param.numRetrieval+1));
%title(hashmethods{j});
end
end