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NTU_subject_feaFV.m
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NTU_subject_feaFV.m
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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 = 'no'; % or 'dplim', or 'no', or 'kmlim';
numClass = 60;
numCluster = 30;
numGmm = 4;
numBlock = 1;
gmmSamRatio = 0.6;
isPCA = 0;
isFVNorm = 0;
isFV = 2;
view_trn = [];
view_tst = [];
sub_trn = [];
sub_tst = [];
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*'])));
data_train = [];
data_test = [];
idx_trn =1;
idx_tst = 1;
lab_trn = [];
lab_tst = [];
dimension = 4096;
dPCA = 4096;
sizeBlock = dimension/numBlock;
%--------- extract the data
for i = [1,2,3,4,5,6,7]%1:length(index_s)%[1,2,3,4,5,6,7]%
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)
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 k<21
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];
view_trn = [view_trn;j];
sub_trn = [sub_trn;j];
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;
view_tst = [view_tst;j];
sub_tst = [sub_tst;j];
end
end
end
end
end
end
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]);
else
sample.train = data_train;
sample.test = data_test;
dPCA = dimension;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
numSam_train = size(data_train,4);
numSam_test = size(data_test,4);
numSam = numSam_train + numSam_test;
sample.all = cat(4,sample.train,sample.test); % help cat [4096 5 11 N]
clear data_train;
clear data_test;
col = 11;row = 1;
%------ cluster patches of training sample in iLayer ------%
%samCluster_all = [reshape(sample.train,dPCA,55*numSam_train)]';
Lab123 = repmat(lab_trn,1,11)';
samImgLabel = Lab123(:); %ÿ¸öÌØÕ÷¶ÔÓ¦µÄ±êÇ©
for iSeg = 1:1
samCluster = [reshape(sample.train(:,iSeg,:,:),dPCA,11*numSam_train)]'; %[N1*11 4096]
if ~strcmp(clusterfun,'no')
else
%------ learning gmm ------%
% samGmm = samCluster;%[N1*11 4096]
% clear samCluster;
% %------ learning gmm ------%
%
% samgmm = samGmm';%[4096 N1*11]
% dsamgmm = samgmm;
% disp('Computing the gmm model......');
% [gmmComp.mean,gmmComp.covariances,gmmComp.priors] = vl_gmm(dsamgmm,numGmm);
% if ~isdir('gmmComp\')
% mkdir('gmmComp\');
% end
% save(['gmmComp\ntu_cs_seg1.mat'],'gmmComp');
% disp('gmmComp have been computed!');
load(['gmmComp\ntu_cs_seg1.mat'],'gmmComp')
%----- FV coding -----%
for iSam = 1:numSam
dSam = [squeeze(sample.all(:,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('finalFea\')
mkdir('finalFea\');
end
if iSam <= numSam_train
feaName = ['FV_feaTr' num2str(iSam) '_seg' num2str(iSeg) ];
else
feaName = ['FV_feaTs' num2str(iSam-numSam_train) '_seg' num2str(iSeg) ];
end
save(['finalFea\',feaName,'.mat'],'feaFV');
end
end
end
clear sample;
%----- training SVM -------%
samTrain = [];
disp('Porcessing all training samples......');
training_label_vector = double(lab_trn);
for i = 1:numSam_train
i
tempSamFea = [];
for iSeg = 1:1
for iBlock = 1:numBlock
feaName = ['FV_feaTr' num2str(i) '_seg' num2str(iSeg) '_iBlock' num2str(iBlock)];
load(['finalFea\',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
i
tempSamFea = [];
for iSeg = 1:1
for iBlock = 1:numBlock
feaName = ['FV_feaTs' num2str(i) '_seg' num2str(iSeg) '_iBlock' num2str(iBlock)];
load(['finalFea\',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);
r_12= [];
r_13 = [];
r_23 = [];
for i = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,22,20,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,56,55,57,58,59,60]
i
% view2 --train
idx_a = find(lab_trn==i);
idx_v2 = find(view_trn==2);
idx = intersect(idx_a,idx_v2);
fv_v2 = samTrain(idx,:);
len2 = length(idx);
% view 3 --train
idx_v3 = find(view_trn==3);
idx = intersect(idx_a,idx_v3);
fv_v3 = samTrain(idx,:);
len3 = length(idx);
%view1 --test
idx_a = find(lab_tst==i);
idx_v1 = find(view_tst==1);
idx = intersect(idx_a,idx_v1);
fv_v1 = samTest(idx,:);
len1 = length(idx);
len = min([len1,len2,len3]);
%===cor compute
r = corrcoef(fv_v1(1:len,:),fv_v3(1:len,:))
r_13 = [r_13; r(1,2)];
r = corrcoef(fv_v1(1:len,:),fv_v2(1:len,:));
r_12 = [r_12; r(1,2)];
r = corrcoef(fv_v2(1:len,:),fv_v3(1:len,:));
r_23 = [r_23; r(1,2)];
end
mean(r_13)
mean(r_12)
mean(r_23)