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Copy pathssh_random_paris6k.m
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ssh_random_paris6k.m
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addpath helpers/
addpath local_data/
if exist('aml') ~= 3
mex -compatibleArrayDims aml.c
end
run ./matconvnet-1.0-beta25/matlab/vl_setupnn.m
main_folder = '/home/wy1314/CBIR_CCTV/'; % oxford5k/ and paris6k/ should be in here
dataset = 'Paris6k_dataset/paris_images'; % dataset to evaluate on
dataset_folder = [main_folder, dataset, '/'];
% --------------------------- parameters of the method ---------------------------
L = 3; % number of levels in the region pyramid of R-MAC
rerank = 1000; %1000 % number of images to re-rank, no re-ranking if 0
step_box = 3; % parameter t in the paper
qratio_t = 1.1; % parameter s in the paper
rf_step = 3; % fixed step for refinement
rf_iter = 5; % number of iterations of refinement
nqe = 5; % number of images to be used for QE
% --------------------------------------------------------------------------------
fprintf('Loading Neural Networks... \n');
load AlexNet
fprintf('Preparing Database... \n');
load rmac_paris6k
%load conv3d_paris6k
fprintf('Preparing Preprocessing... \n');
load eigval_oxford5k
load eigvec_oxford5k
load Xm_oxford5k
fprintf ('Extracing Query Images... \n');
load gnd_paris6k
load bbx_distribution
%bbx_ratio = generate_random_bbx_ratio ('gnd_paris6k',bbx_distribution);
bbx_ratio = generate_specified_bbx_ratio ('gnd_paris6k',0.5);
qimlist = {gnd_paris6k.imlist{gnd_paris6k.qidx}};
[random_bbx] = generate_random_bbx (bbx_ratio,qimlist,dataset_folder);
qim = arrayfun(@(x) crop_qim([dataset_folder, qimlist{x}, '.jpg'], random_bbx(x,:)), 1:numel(gnd_paris6k.qidx), 'un', 0);
fprintf ('Preprocessing Query Features... \n');
qvecs_loc = cellfun(@(x) vecpostproc(mac(x, AlexNet)), qim, 'un', 0); % mac feature vector for all 500 queries
qvecs = cellfun(@(x) vecpostproc(rmac_regionvec(x, AlexNet, L)), qim, 'un', 0); % step 1 and 2 (L2 normed)
qvecs = cellfun(@(x) vecpostproc(apply_whiten (x, Xm_oxford5k, eigvec_oxford5k, eigval_oxford5k)), qvecs, 'un', 0); % step 3 and 4
qvecs = cellfun(@(x) vecpostproc(sum(x, 2)), qvecs, 'un', 0); % step 5 and 6
fprintf('Initial Filtering... \n');
load gnd_paris6k
qvecs = cell2mat(qvecs);
% retrieval with inner product
[sim,ranks] = sort(rmac_paris6k'*qvecs, 'descend');
map = compute_map (ranks, gnd_paris6k.gnd); % can be used in my own code
fprintf('mAP, without re-ranking = %.4f\n', map);
% ----------------------------------------------------------
load conv3d_paris6k.mat
conv3d = conv3d_paris6k;
ranks_rerank = ranks;
ranks_rerank_qe = ranks;
% doing reranking
if rerank
for q = 1:numel(qim)
q
qratio = size(qim{q}, 1) / size(qim{q}, 2);
ids_toplist = ranks(1:rerank, q);
% this version of the code does not support saving of compressed files
% but CNN responses are quantized in the same way to reproduce the results of the paper
conv3d_toplist = arrayfun(@(y)floor((15+((conv3d{y}>=128)*128+conv3d{y}.*(conv3d{y}<128)))/16), ids_toplist, 'un', 0);
% conv3d_toplist is the compressed version of the top-1000 result
% from the initial filtering stage, localization and reranking
% starts here
% perform the localization
bestbox = cellfun(@(x) aml(double(x), int32(10), double(qvecs_loc{q}), qratio, qratio_t, step_box, rf_step, rf_iter), conv3d_toplist, 'un', 0);
% get R-MAC from the localized windows
vecs_bestbox = cellfun(@(x, b) vecpostproc(rmac_regionvec_act(x(b(2):b(3), b(4):b(5), :), L)), conv3d_toplist, bestbox, 'un', 0);
vecs_bestbox = cellfun(@(y) vecpostproc(sum(vecpostproc(apply_whiten(y, Xm_oxford5k, eigvec_oxford5k, eigval_oxford5k)), 2)), vecs_bestbox, 'un', 0);
% re-compute similarity and re-rank
scores_rerank = qvecs(:, q)'*cell2mat(vecs_bestbox');
[~, idx] = sort(scores_rerank, 'descend');
ranks_rerank(1:rerank, q) = ranks_rerank(idx, q);
% perform average query expansion
scores_rerank_qe = mean([cell2mat({vecs_bestbox{idx(1:nqe)}}), qvecs(:, q)]')*cell2mat(vecs_bestbox');
[~, idx] = sort(scores_rerank_qe, 'descend');
ranks_rerank_qe(1:rerank, q) = ranks_rerank_qe(idx, q);
end
end
% mAP computation
map = compute_map (ranks_rerank, gnd_oxford5k.gnd);
fprintf('mAP, after re-ranking = %.4f\n', map);
map = compute_map (ranks_rerank_qe, gnd_oxford5k.gnd);
fprintf('mAP, after re-ranking and QE = %.4f\n', map);
ranks = ranks_rerank_qe;
% ----------------------------------------------------------
query = floor ( rand * numel ( gnd_paris6k.qidx ));
figure;
im_index = gnd_paris6k.qidx(query);
image = imread ( strcat(dataset_folder,gnd_paris6k.imlist{im_index} ,'.jpg'));
imshow(image);
bounding_box_tmp = random_bbx(query,:);
bounding_box = zeros (1,4);
bounding_box (1) = bounding_box_tmp(1);
bounding_box (2) = bounding_box_tmp(2);
bounding_box (3) = bounding_box_tmp(3) - bounding_box_tmp(1);
bounding_box (4) = bounding_box_tmp(4) - bounding_box_tmp(2);
rectangle ('Position',bounding_box ,'EdgeColor','g','LineWidth',2);
title (strcat('query',string(query)));
xlabel (strcat ('crop ratio',' ',string (bbx_ratio(query))));
figure;
for i = 1 : 6
[image , map ] = imread(strcat(dataset_folder,gnd_paris6k.imlist{ranks(i,query)},'.jpg'));
subplot(2,3,i), imshow(image ,map)
if ismember (ranks(i,query),gnd_paris6k.gnd(query).ok )
rectangle ('Position', [1 1 size(image,2)-1 size(image,1)-1] ,'EdgeColor','g','LineWidth',2);
elseif ismember (ranks(i,query),gnd_paris6k.gnd(query).junk )
rectangle ('Position', [1 1 size(image,2)-1 size(image,1)-1] ,'EdgeColor','b','LineWidth',2);
else
rectangle ('Position', [1 1 size(image,2)-1 size(image,1)-1] ,'EdgeColor','r','LineWidth',2);
end
end