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ntu_cross_view_mining_new.m
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ntu_cross_view_mining_new.m
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%clear all;
addpath(genpath('liblinear-2.1'));
addpath('general_function\');
addpath('libraryPubliced\tool\liblinear\');
addpath('libraryPubliced\tool\liblinear\matlab\');
%addpath('libraryPubliced\tool\libsvm\matlab\');
run('I:\wangyancheng\code\vlfeat-0.9.21\toolbox\vl_setup');
clusterfun = 'kmlim'; % or 'dplim', or 'no', or 'kmlim';
numClass = 60;
numCluster_list = [30 30 30 30 30 30 30 30 30 ];
numClSelect = 2;
numGmm = 4;
numBlock = 1;
gmmSamRatio = 0.6;
tim = []
% seg_c = [1 2 3 4 5];
seg_c = [1 2 3 4 5];
isPCA = 0;
isFVNorm = 0;
isFV = 0;
isMerge =0;
path = 'G:\wangyancheng\ntu_rgbd\dynamic\feature_ntu_view_share_MBB\';% data path
index_s = ['01';'02';'03';'04';'05';'06';'07';'08';'09';'10';'11';'12';'13';'14';'15';'16';'17'];
index_c = ['01';'02';'03'];
index_p = ['01';'02';'04';'05';'08';'09';'13';'14';'15';'16';'17';'18';'19';'25';'27';'28';'31';'34';'35';'38';...
'03';'06';'07';'10';'11';'12';'20';'21';'22';'23';'24';'26';'29';'30';'32';'33';'36';'37';'39';'40'];%
index_r = ['01';'02'];
index_a = ['01';'02';'03';'04';'05';'06';'07';'08';'09';'10';'11';'12';'13';'14';'15';'16';'17';'18';'19';'20';...
'21';'22';'23';'24';'25';'26';'27';'28';'29';'30';'31';'32';'33';'34';'35';'36';'37';'38';'39';'40';...
'41';'42';'43';'44';'45';'46';'47';'48';'49';'50';'51';'52';'53';'54';'55';'56';'57';'58';'59';'60'];
%data_train = zeros(4096,5,11,length(dir([path '*_v01*']))+length(dir([path '*_v02*'])));
dimension = 4096;
if isPCA dPCA = 1024; else dPCA=dimension; end
sizeBlock = dPCA/numBlock;
new_path1 = 'D:\wangyancheng\code\ntudata';
if 0
%--------- extract the data
flag = [];
data_train = [];
data_test = [];
idx_trn =1;
idx_tst = 1;
lab_trn = [];
lab_tst = [];
fileNames_Tr = [];
if ~exist(new_path1)
mkdir(new_path1); % 若不存在，在当前目录中产生一个子目录‘Figure’
end
fd= fopen('save_name.txt', 'w');
%--------- extract the data
for i = 1:1%length(index_s)
i
for j = 1:length(index_c)
for k = 1:length(index_p)
for m =1:length(index_r)
for d = 1:length(index_a)%-50
filename = ['S0',index_s(i,:), 'C0' ,index_c(j,:),'P0',index_p(k,:),'R0',index_r(m,:),'A0',index_a(d,:),'.mat'];
if exist([path filename],'file')
if j>1
fprintf(fd,'%s\r\n',filename);
fileNames_Tr = [fileNames_Tr;filename];
dt_train1 = load([path filename]);
data = reshape(cell2mat(dt_train1.data),[4096,5,11,2]);
data_train(:,:,:,idx_trn) = squeeze(data(:,:,:,1));
idx_trn = idx_trn+1;
lab_trn = [lab_trn;d];
% in order to split the trainset
if(k>35)
flag = [flag;1];
else
flag = [flag;0];
end
else
dt_test = load([path filename]);
data = reshape(cell2mat(dt_test.data),[4096,5,11,2]);
data_test(:,:,:,idx_tst) = squeeze(data(:,:,:,1));
lab_tst = [lab_tst;d];
idx_tst = idx_tst+1;
end
end
end
end
end
end
end
fclose(fd);
end
%------- PCA --------%
% if isPCA
% fprintf('----PCA----\n');
% dt = reshape(data_train,[4096 5*11*size(data_train,4)]);
% [pc,score,latent,tsquare] = pca(dt');
% after_pca = score(:,1:dPCA);
%
% dt = reshape(data_test,[4096 5*11*size(data_test,4)]);
% test_pca = dt' * pc(:,1:dPCA);
% sample.train = reshape(after_pca',[dPCA 5 11 numSam_train]);
% sample.test = reshape(test_pca',[dPCA 5 11 numSam_test]);
%
% end
numSam_train = size(data_train,4);
numSam_test = size(data_test,4);
size(data_train)
size(data_test)
% sample.all = cat(4,data_train,data_test); % help cat [4096 5 11 N]
%
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% for i=1:size(data_train,2)
%
% filename1 = ['ntu_datamat_',num2str(i),'.mat'];
% if ~exist([new_path1 '\' filename1])
%
% sub_sample = cat(3,squeeze(data_train(:,i,:,:)),squeeze(data_train(:,i,:,:)));
% save([new_path1 '\' filename1],'sub_sample','lab_trn','numSam_train','numSam_test','flag','lab_tst','-v7.3');
% end
% end
% clear data_train;
%
% clear data_test;
% end
% merge for segmation
se = 5;
%sample = Normlize(sample);
samp = [];
% if isMerge
% for p = 2:4
% for d = (p+1):5
% p_max = sample.train(:,d,:,:)-sample.train(:,p,:,:);
% sample.train = [sample.train p_max];
% p_max = sample.test(:,d,:,:)-sample.test(:,p,:,:);
% sample.test = [sample.test p_max];
% end
% end
% se=9;
% end
% sample.train = samp(:,:,:,1:numSam_train);
% sample.test = samp(:,:,:,numSam_train+1:end);
% sample.all = [];
% sample.all = samp;
%
% sample.train = sample.all(:,:,:,1:numSam_train);
% sample.test = sample.all(:,:,:,numSam_train+1:end);
% col = 11;row = 1;
% sam_instance = reshape(sample.all(:,:,:,1:numSam_train),[4096 size(sample.all,2) 11*numSam_train]);
% lab_instan = repmat(lab_trn,[1 11])';
% lab_instan = lab_instan(:);
%tst_instance = reshape(sample.all(:,:,:,numSam_train+1:end),[4096 size(sample.all,2) 11*numSam_test]);
%------ cluster patches of training sample in iLayer ------%
%samCluster_all = [reshape(sample.train,dPCA,55*numSam_train)]';
% Lab123 = repmat(lab_trn,1,11)';
% samImgLabel = Lab123(:); %每个特征对应的标签
aaa = [];
for iSeg = seg_c%[1 6 7 8 9]%1:se
iSeg
numSam = numSam_train + numSam_test;
samCluster = reshape(data_train(:,iSeg,:,:),[4096 11*numSam_train]);
lab_instan = repmat(lab_trn,[1 11])';
lab_instan = lab_instan(:);
samCluster = samCluster'; %[N1*11 4096]
if ~strcmp(clusterfun,'no')&&(numCluster_list(iSeg)~= 0)
numCluster = numCluster_list(iSeg);
if strcmp(clusterfun,'dplim')
res_cluster = func_dpCluster(samCluster,numCluster);
end
if strcmp(clusterfun,'kmlim')
new_path2 = 'I:\wangyancheng\code\ntu\cluster_data';
if ~exist(new_path2)
mkdir(new_path2); % 若不存在，在当前目录中产生一个子目录
end
filename = ['k' num2str(numCluster) '_s_test' num2str(iSeg) '.mat'];
if ~exist([new_path2 '\' filename])
res_cluster = func_kmCluster1(samCluster,numCluster,1000,1);
save([new_path2 '\' filename],'res_cluster');
else
load([new_path2 '\' filename])
end
end
cluster_Label = res_cluster.labels;
%% find the cluster center sample
if 0
dSam=samCluster;%[numSam,~]
cluster_center =res_cluster.centersFea;%[numCenter,~]
samDist = func_distCenter(dSam,cluster_center); %NxC
for i = 1:30
cluster_indx = find(samDist(:,i) == min(samDist(:,i)), 1 );
file_idx = floor(cluster_indx/11);
view_idx = mod(cluster_indx,11)
fileNames_Tr(file_idx,:)
disp('------------------');
end
end
ratioEachCl= zeros(30,60);
ratioEachClass = zeros(30,60);
%% Global
for i = 1:numCluster
numInCluster(i) = length(find(cluster_Label == i)); %各个簇所含特征数量
for j = 1:numClass
idxClassJ = find(lab_instan == j); %标签j在所有特征中的位置
clusterLabelForClassJ = cluster_Label(idxClassJ); %标签j下的特征所属簇 找到标签j的簇label
numEachClInClass(i,j) = length(find(clusterLabelForClassJ == i)); %第i个簇中第j标签的特征数量
ratioEachClass(i,j) = numEachClInClass(i,j)/length(idxClassJ);
end
ratioEachCl(i,:) = numEachClInClass(i,:) / numInCluster(i); % 每个簇在不同类别下的分布
ratioEachCl(i,ratioEachCl(i,:)==0 ) = 0.0001;
globalEntropy(i) = 1 + sum(ratioEachCl(i,:).*log(ratioEachCl(i,:)))/log(numClass);
end
%% Local
HI_score = []
IJ = [];
for i = 1:numClass
for j = i+1:numClass
HI_score = [HI_score, 1-sum(min(ratioEachClass(:,i),ratioEachCl(:,j)))];
IJ = [IJ;i,j];
end
end
id = HI_score>0.95;
a_l = IJ(id,:);
a_l = a_l(:);
B = unique(a_l);
c = zeros(length(B),1);
[a,id] = sort(c);
Dis_F = 1
if length(id)>10
numLocal = 10;
elseif length(id)>0
numLocal = length(id);
else
Dis_F = 0
end
id = id(end-int8(numLocal)+1:end);
localClass = B(id);
if Dis_F
for k = 1:numCluster
ratioLocalCl = numEachClInClass(k,localClass) / numInCluster(k);
ratioLocalCl(ratioLocalCl==0) = 0.0001;
ratioLocalCl = ratioLocalCl/sum(ratioLocalCl);
localEntropy(k) = 1 + sum(ratioLocalCl.*log2(ratioLocalCl))/log2(length(localClass));
end
else
localEntropy = 0;
end
useLocal=0;
if useLocal ==1
beta = 0.3;
else
beta=0;
end
gamma = 0.016;
idx_discard = [];
%score(i,:) = globalEntropy.*(beta+localEntropy(i,:));
% score(i,:) = globalEntropy;
% gamma = 0.004 ;% (1-class_acc(i))/15;
% gamma = 0.025 - find(idx_rank==i)/400;
% idxlocal = find(localEntropy(i,:)<gamma);
% idx_c = idxlocal(globalEntropy(idxlocal)<0.1);
score = (1-beta)*globalEntropy + beta*localEntropy;
% [scorePattern idxscore] = sort(Score(i,:));
% idx_c = find(score(i,:) <gamma); %找到得分小于阈值的pattern
[a,id] = sort(score);
idx_c = id(1);
for k = 1:length(idx_c)
idx = find(cluster_Label==idx_c(k));
idx_discard = [idx_discard; idx]; %找到sam_instance中最终要丢弃的instance
end
aaa(iSeg) = length(idx_discard)
zz = randperm(length(idx_discard));
%idx_discard = idx_discard(zz(1:length(idx_discard)/2))';
%%%%%%%%%%%%%% used to select sample for GMM
%------ select patches ------%
samGmm = samCluster;
samGmm(idx_discard,:) = [];
clear samCluster;
%------ learning gmm ------%
disp('Computing the gmm model......');
[gmmComp.mean,gmmComp.covariances,gmmComp.priors] = vl_gmm(samGmm',numGmm);%[4096 N1*11]
if ~isdir('gmmComp\')
mkdir('gmmComp\');
end
save(['gmmComp\' 'resGmm_' num2str(iSeg) '.mat'],'gmmComp');
disp('gmmComp have been saved!');
clear samGmm
%------ test instance select ------%
%%%%%%%%%%%%%%% used to select sample for FV
%----- FV coding -----%
b = ones(numSam_train*11,1);
b(idx_discard) = 0;
b = reshape(b,11,numSam_train)';
for iSam = 1:numSam_train
dSam = squeeze(data_train(:,iSeg,:,iSam))';
dSam(b(iSam,:)==0,:)=[];
if isFVNorm
feaFV = vl_fisher(dSam',gmmComp.mean,gmmComp.covariances,gmmComp.priors,'Normalized');
else
feaFV = vl_fisher(dSam',gmmComp.mean,gmmComp.covariances,gmmComp.priors,'Improved');
end
%-- store the fisher vector---%
if ~isdir('finalFeaTR\')
mkdir('finalFeaTR\');
end
feaName = ['FV_feaTr' num2str(iSam) '_seg' num2str(iSeg)];
save(['finalFeaTR\',feaName,'.mat'],'feaFV');
disp(['Got ',num2str(iSam),'/',num2str(numSam),'--',num2str(iSeg),num2str(se) ' samples' ' final feature data .....']);
end
for iSam= 1:numSam_test
if ~isdir('finalFeaTs\')
mkdir('finalFeaTs\');
end
tic
idxCenterChoose =idx_c;
fvSam = squeeze(data_test(:,iSeg,:,iSam))';
samDist = func_distCenter(fvSam,res_cluster.centersFea);
for i = 1:11
samLabel(i) = find(samDist(i,:) == min(samDist(i,:)), 1 );
end
idxSelect = find(samLabel == idxCenterChoose);
if length(idxSelect) >4
iidx = randperm(length(idxSelect));
nnumSel = 4;
idxSelect = idxSelect(iidx(1:nnumSel));
end
fvSam(idxSelect,:) = [];
tim = [tim;toc];
feaFV = vl_fisher(fvSam',gmmComp.mean,gmmComp.covariances,gmmComp.priors,'Improved');
feaName = ['FV_feaTs' num2str(iSam) '_seg' num2str(iSeg)];
save(['finalFeaTs\',feaName,'.mat'],'feaFV');
end
else
% samGmm = samCluster;%[N1*11 4096]
% clear samCluster;
%------ learning gmm ------%
samgmm = samCluster';%[4096 N1*11]
disp('Computing the gmm model......');
[gmmComp.mean,gmmComp.covariances,gmmComp.priors] = vl_gmm(samgmm,numGmm);
if ~isdir('gmmComp\')
mkdir('gmmComp\');
end
%%save(['gmmComp\' 'resGmm_' num2str(iSeg) '.mat'],'gmmComp');
%disp('gmmComp have been saved!');
disp('gmm have been kearned!');
%----- FV coding -----%
for iSam = 1:numSam_train
dSam = squeeze(data_train(:,iSeg,:,iSam))';
if isFVNorm
feaFV = vl_fisher(dSam',gmmComp.mean,gmmComp.covariances,gmmComp.priors,'Normalized');
else
feaFV = vl_fisher(dSam',gmmComp.mean,gmmComp.covariances,gmmComp.priors,'Improved');
end
%-- store the fisher vector---%
if ~isdir('finalFeaTR\')
mkdir('finalFeaTR\');
end
feaName = ['FV_feaTr' num2str(iSam) '_seg' num2str(iSeg)];
save(['finalFeaTR\',feaName,'.mat'],'feaFV');
disp(['Got ',num2str(iSam),'/',num2str(numSam),'--',num2str(iSeg),num2str(se) ' samples' ' final feature data .....']);
end
for iSam= 1:numSam_test
if ~isdir('finalFeaTs\')
mkdir('finalFeaTs\');
end
dSam = squeeze(data_test(:,iSeg,:,iSam))';
feaFV = vl_fisher(dSam',gmmComp.mean,gmmComp.covariances,gmmComp.priors,'Improved');
feaName = ['FV_feaTs' num2str(iSam) '_seg' num2str(iSeg)];
save(['finalFeaTs\',feaName,'.mat'],'feaFV');
end
end
clear sub_sample
end
clear feaName feaFV dsamgmm gmmComp samgmm b
%----- training SVM -------%
samTrain = [];
disp('Porcessing all training samples......');
training_label_vector = double(lab_trn);
for i = 1:numSam_train
i
tempSamFea = [];
for iSeg = seg_c
for iBlock = 1:numBlock
feaName = ['FV_feaTr' num2str(i) '_seg' num2str(iSeg)];
load(['finalFeaTR\',feaName,'.mat'],'feaFV');
tempSamFea = [tempSamFea; feaFV];
end
end
tempSamFea = tempSamFea';
samTrain = [samTrain;tempSamFea];
end
training_instance_sparse = sparse(double(samTrain));
disp('Training svm model......');
svmModel = train(training_label_vector, training_instance_sparse);
samTest = [];
disp('Processing all testing samples......');
testing_label_vector = lab_tst;
for i = 1:numSam_test
tempSamFea = [];
for iSeg = seg_c%1:se
for iBlock = 1:numBlock
feaName = ['FV_feaTs' num2str(i) '_seg' num2str(iSeg)];
load(['finalFeaTs\',feaName,'.mat'],'feaFV');
tempSamFea = [tempSamFea; feaFV];
end
end
tempSamFea = tempSamFea';
samTest = [samTest;tempSamFea];
end
testing_instance_sparse = sparse(double(samTest));
disp('Testing......');
[predicted_label,accuracy,decision_values] = ...
predict(testing_label_vector, testing_instance_sparse, svmModel);
% predict_label= predicted_label;
%
% for i = 1:numClass
% num_in_class(i) = length(find(lab_tst==i));
%
% end
% name_class = importdata('classInd.txt');
% for ci = 1:numClass
% for cj = 1:numClass
% c_end = sum(num_in_class(1:ci));
% c_start = c_end - num_in_class(ci)+1;
% confusion_matrix(ci,cj)=length(find(predict_label(c_start:c_end)==cj))/num_in_class(ci);
%
% end
% end
%
% draw_cm(confusion_matrix,name_class,numClass);