京东金融大数据竞赛猪脸识别-方法选取
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由于可用的特征提取和分类器构造方法较多，为了确定选取方法。现在在视频提取的图像上进行算法验证，取这些图像的一部分进行训练，剩余部分进行测试，并进行交叉验证。找到准确率最高的方法后再将其用于测试集。
以reco_toolbox为例，下面给出方法选取的代码：
clc,close all, clear ,drawnow
database_name = {‘JDPig‘ };
database_ext = {‘jpg‘ , ‘jpg‘ , ‘png‘};
descriptors_name = {‘denseSIFT_mlhbow_spyr‘ , ‘denseSIFT_dl_spyr‘ , ‘denseSIFT_mlhlcc_spyr‘ ,....
‘denseCOLOR_mlhbow_spyr‘ , ‘denseCOLOR_dl_spyr‘ , ‘denseCOLOR_mlhlcc_spyr‘ , ...
‘densePATCH_mlhbow_spyr‘ , ‘densePATCH_dl_spyr‘ , ‘densePATCH_mlhlcc_spyr‘ , ...
‘denseMBLBP_mlhbow_spyr‘ , ‘denseMBLBP_dl_spyr‘ , ‘denseMBLBP_mlhlcc_spyr‘ , ...
‘denseMBLDP_mlhbow_spyr‘ , ‘denseMBLDP_dl_spyr‘ , ‘denseMBLDP_mlhlcc_spyr‘ , ...
‘mlhoee_spyr‘ , ‘mlhmslsd_spyr‘ , ‘mlhmslbp_spyr‘ , ‘mlhmsldp_spyr‘};
classifier = {‘liblinear‘ , ‘pegasos‘ , ‘libsvm‘};
choice_database = [1];
%choice_descriptors = [8];
choice_descriptors = [18];
choice_classifier = [1]; %Libnear=1/Pegasos=2/Libsvm=3
data_name = database_name{choice_database(1)};
im_ext = database_ext{choice_database(1)};
rootbase_dir = pwd;
images_dir = fullfile(pwd , ‘images‘ , data_name);
core_dir = fullfile(pwd , ‘core‘);
feat_dir = fullfile(pwd , ‘features‘);
models_dir = fullfile(pwd , ‘models‘);
addpath(core_dir)
dir_image = dir(images_dir);
nb_topic = length(dir_image) - 2;
classe_name = cellstr(char(dir_image(3:nb_topic+2).name))‘;
%% Training Parameters %%
K = 1; % 交叉验证折数
seed_value = 5489;
post_norm = 0;
do_weightinglearning = [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1];
uselogic = 1;
fusion_method = 1; %max=0/mean=1
nbin = 100; % for ROC curves
nb_images_per_topic = zeros(1 , nb_topic);
y = [];
for i = 1:nb_topic
nb_images_per_topic(i) = length(dir(fullfile(pwd , ‘images‘ , data_name , dir_image(i+2).name , [‘*.‘ , im_ext])));
y = [y , i*ones(1 , nb_images_per_topic(i))];
end
N = sum(nb_images_per_topic);
base = cell(1 , length(database_name));
base{1}.maxperclasstrain = nb_images_per_topic;
eval([data_name , ‘_config_classifier‘]);
s = RandStream.create(‘mt19937ar‘,‘seed‘,seed_value);
RandStream.setGlobalStream(s);
Itrain = zeros(K , sum(base{choice_database}.maxperclasstrain));
for j = 1:K
cotrain = 1;
for i = 1:nb_topic
indi = find(y==i);
tempind = randperm(nb_images_per_topic(i));
indtrain = tempind(1:base{choice_database}.maxperclasstrain(i));
Itrain(j , cotrain:cotrain+base{choice_database}.maxperclasstrain(i)-1) = indi(indtrain);
cotrain = cotrain + base{choice_database}.maxperclasstrain(i);
end
end
prior = nb_images_per_topic‘/length(y);
nb_descriptors = length(choice_descriptors);
nb_classifier = length(choice_classifier);
auc = zeros(nb_topic , K , nb_classifier , nb_descriptors );
tp = zeros(nb_topic , nbin , K , nb_classifier , nb_descriptors );
fp = zeros(nb_topic , nbin , K , nb_classifier , nb_descriptors );
perf = zeros(K , nb_classifier , nb_descriptors );
perf_topic = zeros(nb_topic , K , nb_classifier , nb_descriptors );
reco_topic = zeros(nb_topic , K , nb_classifier , nb_descriptors );
matconf = zeros(nb_topic , nb_topic , K , nb_classifier , nb_descriptors );
probaR = zeros(nb_topic , nb_topic , nb_classifier , nb_descriptors );
model = cell(1 , nb_topic);
RR = zeros( K , nb_classifier , nb_descriptors );
for d = 1 : nb_descriptors
cdescriptors = choice_descriptors(d);
base_descriptor = descriptors_name{cdescriptors};
for c = 1:nb_classifier
ccurrent = choice_classifier(c);
base_classifier = classifier{ccurrent};
base_name = [data_name , ‘_‘ , base_descriptor];
base_name_model = [data_name , ‘_‘ ,base_descriptor , ‘_‘ , base_classifier];
fprintf(‘\nLoad descriptor %s for classifier = %s\n\n‘ , base_name , base_classifier );
drawnow
clear X y
load(fullfile(feat_dir , base_name ));
if(post_norm == 1)
sumX = sum(X , 1) + 10e-8;
X = X./sumX(ones(size(X , 1) , 1) , :);
end
if(post_norm == 2)
sumX = sum(X , 1);
temp = sqrt(sumX.*sumX + 10e-8);
X = X./temp(ones(size(X , 1) , 1) , :);
end
if(param_classif{cdescriptors,ccurrent}.n > 0)
fprintf(‘Homoegeous Feature Kernel Map with n = %d, L = %4.2f, kernel = %d\n\n‘, param_classif{cdescriptors,ccurrent}.n , param_classif{cdescriptors,ccurrent}.L , param_classif{cdescriptors,ccurrent}.kerneltype);
drawnow
X = homkermap(X , param_classif{cdescriptors,ccurrent});
end
for k = 1:K
Xtrain = X(: , Itrain(k , :));
ytrain = y(Itrain(k , :));
fprintf(‘\nLearn train data for classifier = %s and descriptor = %s\n\n‘ , base_classifier , base_name);
drawnow
modelName = fullfile(models_dir , base_name_model )
if ~exist(modelName,‘file‘)
for t = 1:nb_topic
ind_topic = (ytrain==t);
ytopic = double(ind_topic);
ytopic(ytopic==0) = -1;
if((strcmp(base_classifier , ‘liblinear‘)) )
fprintf(‘cv = %d/%d, learn topic = %s (%d/%d), h1 = %10.5f for classifier = %s and descriptor = %s \n‘ , k , K , classe_name{t} , t , nb_topic , param_classif{cdescriptors,ccurrent}.c , base_classifier , base_descriptor)
drawnow
if(do_weightinglearning(c))
npos = sum(ytopic==1);
nneg = length(ytopic) - npos;
wpos = nneg/npos;
options = [‘-q -s ‘ num2str(param_classif{cdescriptors,ccurrent}.s) ‘ -B ‘ num2str(param_classif{cdescriptors,ccurrent}.B) ‘ -w1 ‘ num2str(wpos) ‘ -c ‘ num2str(param_classif{cdescriptors,ccurrent}.c)];
else
options = [‘-q -s ‘ num2str(param_classif{cdescriptors,ccurrent}.s) ‘ -B ‘ num2str(param_classif{cdescriptors,ccurrent}.B) ‘ -c ‘ num2str(param_classif{cdescriptors,ccurrent}.c)];
end
model{t} = train_dense(ytopic‘ , Xtrain , options , ‘col‘);
[ytopic_est , accuracy_test , ftopic] = predict_dense(ytopic‘ , Xtrain , model{t} , ‘-b 0‘ , ‘col‘); % test the training data
if(uselogic)
options = [‘-q -s 0 -B ‘ num2str(param_classif{cdescriptors,ccurrent}.B) ‘ -c ‘ num2str(param_classif{cdescriptors,ccurrent}.c)];
model{t}.logist = train_dense(ytopic‘ , ftopic‘ , options , ‘col‘);
else
[A , B] = sigmoid_train(ytopic , ftopic‘);
ptopic = sigmoid_predict(ftopic‘ , A , B);
model{t}.A = A;
model{t}.B = B;
end
end
if ((strcmp(base_classifier , ‘pegasos‘)) )
fprintf(‘cv = %d, learn topic = %s (%d/%d), h1 = %10.5f for classifier = %s and descriptor = %s \n‘ , k , classe_name{t} , t , nb_topic , param_classif{cdescriptors,ccurrent}.lambda , base_classifier , base_descriptor)
drawnow
model{t}.w = homker_pegasos_train(Xtrain , ytopic , param_classif{cdescriptors,ccurrent});
if(uselogic)
options = [‘-q -s 0 -B ‘ num2str(param_classif{cdescriptors,ccurrent}.biasmult) ‘ -c ‘ num2str(1/(param_classif{cdescriptors,ccurrent}.lambda*N))];
model{t}.logist = train_dense(ytopic‘ , ftopic , options , ‘col‘);
else
[A , B] = sigmoid_train(ytopic , ftopic);
ptopic = sigmoid_predict(ftopic‘ , A , B);
model{t}.A = A;
model{t}.B = B;
end
end
if((strcmp(base_classifier , ‘libsvm‘)) )
fprintf(‘cv = %d, learn topic = %s (%d/%d), h1 = %10.5f for classifier = %s and descriptor = %s \n‘ , k , classe_name{t} , t , nb_topic , param_classif{cdescriptors,ccurrent}.c , base_classifier , base_descriptor)
drawnow
if(do_weightinglearning(c))
npos = sum(ytopic==1);
nneg = length(ytopic) - npos;
wpos = nneg/npos;
options = [‘-s ‘ num2str(param_classif{cdescriptors,ccurrent}.s) ‘ -t ‘ num2str(param_classif{cdescriptors,ccurrent}.t) ‘ -w1 ‘ num2str(wpos) ‘ -c ‘ num2str(param_classif{cdescriptors,ccurrent}.c)];
else
options = [‘-s ‘ num2str(param_classif{cdescriptors,ccurrent}.s) ‘ -t ‘ num2str(param_classif{cdescriptors,ccurrent}.t) ‘ -c ‘ num2str(param_classif{cdescriptors,ccurrent}.c)];
end
model{t} = svmtrain(ytopic‘ , Xtrain‘ , options );
[ytopic_est , accuracy_test , ftopic] = svmpredict(ytopic‘ , Xtrain‘ , model{t}); % test the training data
if(uselogic)
options = [‘-q -s 0 -c ‘ num2str(param_classif{cdescriptors,ccurrent}.c)];
model{t}.logist = train_dense(ytopic‘ , ftopic , options);
else
[A , B] = sigmoid_train(ytopic , ftopic);
ptopic = sigmoid_predict(ftopic , A , B);
model{t}.A = A;
model{t}.B = B;
end
end
end
save(modelName,‘model‘);
clear Xtrain ytrain X;
else
load(modelName,‘model‘);
end
load(fullfile(feat_dir , ‘JDTest_mlhmslbp_spyr‘ ));
Xtest = X;
ytopic = ones(length(XTest),1);
fprintf(‘\nPredict test data for classifier = %s and descriptor = %s\n\n‘ , base_classifier , base_name);
drawnow
for t = 1:nb_topic
fprintf(‘cv = %d, predict topic = %s (%d/%d) for classifier = %s and descriptor = %s\n‘ , k , classe_name{t} , t , nb_topic , base_classifier , base_descriptor);
drawnow
if((strcmp(base_classifier , ‘liblinear‘)) )
[ytopic_est , accuracy_test , ftopic ] = predict_dense(ytopic‘ , Xtest , model{t} , ‘-b 0‘ , ‘col‘); % test the training data
if(uselogic)
[l2,a2,d2] = predict_dense(ytopic‘ , ftopic , model{t}.logist , ‘-b 1‘);
ptopic = d2(:,find(model{t}.logist.Label==1))‘;
else
ptopic = sigmoid_predict(ftopic‘ , model{t}.A , model{t}.B);
end
end
if ((strcmp(base_classifier , ‘pegasos‘)) )
ftopic = homker_predict( Xtest , model{t}.w , param_classif{cdescriptors,ccurrent}); % test the training data
if(uselogic)
[l2,a2,d2] = predict_dense(ytopic‘ , ftopic‘ , model{t}.logist , ‘-b 1‘);
ptopic = d2(:,find(model{t}.logist.Label==1))‘;
ytopic_est = double(ptopic > 0.5)‘;
ytopic_est(ytopic_est==0) = -1;
else
ptopic = sigmoid_predict(ftopic , model{t}.A , model{t}.B);
ytopic_est = double(ptopic > 0.5)‘;
ytopic_est(ytopic_est==0) = -1;
end
end
if((strcmp(base_classifier , ‘libsvm‘)) )
[ytopic_est , accuracy_test , ftopic ] = svmpredict(ytopic‘ , Xtest‘ , model{t}); % test the training data
if(uselogic)
[l2,a2,d2] = predict_dense(ytopic‘ , ftopic , model{t}.logist , ‘-b 1‘);
ptopic = d2(:,find(model{t}.logist.Label==1))‘;
else
ptopic = sigmoid_predict(ftopic‘ , model{t}.A , model{t}.B);
end
end
[rec_topic , mat_conf ] = perf_classif(ytopic , ytopic_est‘);
proba(t , : , k,c,d) = ptopic;
reco_topic(t , k , c , d) = rec_topic;
[tp(t,:,k,c,d) , fp(t,:,k,c,d)] = basicroc(ytopic , ptopic );
auc(t,k,c,d) = auroc(tp(t,:,k,c,d)‘, fp(t,:,k,c,d)‘);
end
[maxi , ytest_est] = max(proba(: , : , k , : , :) , [], 1);
RR(k,c,d) = sum(ytest == ytest_est(:,:,:,c,d))/Ntest;
[val , matconf(: , : , k , c , d)] = perf_classif(ytest , ytest_est(:,:,:,c,d));
for t = 1:nb_topic
ind = find(ytest == t);
perf_topic(t , k , c , d) = sum(ytest_est(:,ind,:,c,d)==t)/length(ind);
end
clear Xtest ytest;
fprintf(‘\nFor cv = %d, Test perf = %5.3f\n‘ , k , RR(k,c,d));
drawnow
end
end
end
mean_reco_multi = squeeze(mean(RR , 1));
std_reco_multi = squeeze(std(RR , 0 , 1));
temp_1vsall = mean(perf_topic, 1);
mean_reco_1vsall = squeeze(mean(temp_1vsall , 2));
std_reco_1vsall = squeeze(std(temp_1vsall , 0 , 2));
fprintf((repmat(‘\nReco multi = %4.3f +/- %4.3f\n‘, 1 , length(mean_reco_multi) )) , [mean_reco_multi , std_reco_multi]‘)
fprintf((repmat(‘\nReco 1vsall = %4.3f +/- %4.3f\n‘, 1 , length(mean_reco_multi) )) , [mean_reco_1vsall , std_reco_1vsall]‘);
tpmean = reshape(mean(mean(tp , 1) , 3) , [nbin , nb_classifier*nb_descriptors]);
fpmean = reshape(mean(mean(fp , 1) , 3) , [nbin , nb_classifier*nb_descriptors]);
auc_mean = squeeze(mean(mean(auc , 1) , 2));
if(fusion_method == 0)
fusion = prod(prod(proba , 5) , 4);
else
fusion = mean(mean(proba , 5) , 4);
end
[maxifusion , yfusion_est] = max(fusion , [] , 1);
reco_fusion_multi = mean(sum(permute(yfusion_est , [3 , 2 , 1]) == y(Itest) , 2)/Ntest);
fprintf(‘\nReco fusion multi = %4.3f\n‘ , reco_fusion_multi)
temp_fusion = zeros(nb_topic , K);
temp_fusion2 = zeros(nb_topic , K);
for k = 1:K
ytest = y(Itest(k , :));
for t = 1:nb_topic
ind_topic = (ytest==t);
ytopic = double(ind_topic);
ytopic(ytopic==0) = -1;
ytopic_est = double(fusion(t , : , k) > 0.5);
ytopic_est(ytopic_est==0) = -1;
temp_fusion(t , k) = sum(ytopic_est == ytopic)/length(ytopic);
[tpfusion(t,:,k) , fpfusion(t,:,k)] = basicroc(ytopic , fusion(t , : , k) );
aucfusion(t,k) = auroc(tpfusion(t,:,k)‘, fpfusion(t,:,k)‘);
ind = find(ytest==t);
temp_fusion2(t,k) = sum(yfusion_est(1,ind,k) == ytest(ind))/length(ytest(ind));
end
end
tpfusion_mean = squeeze(mean(mean(tpfusion , 3) , 1));
fpfusion_mean = squeeze(mean(mean(fpfusion , 3) , 1));
aucfusion_mean = mean(mean(aucfusion , 2) , 1);
perf_topic_fusion_mean = mean(temp_fusion2 , 2);
perf_topic_fusion_std = std(temp_fusion2 , 0 , 2);
reco_fusion_1vsall = mean(mean(temp_fusion));
fprintf(‘\nReco fusion = %4.3f\n‘ , reco_fusion_1vsall)
[indd ,indc] = meshgrid(choice_descriptors , choice_classifier );
toto = cell(nb_classifier*nb_descriptors,1);
for i = 1:nb_classifier*nb_descriptors
toto{i} = [classifier{indc(i)} , ‘ ‘ , descriptors_name{indd(i)} , ‘, AUC= ‘ num2str(auc_mean(i))];
end
figure(1)
if(nb_descriptors*nb_classifier == 1)
plot(fpmean , tpmean , ‘linewidth‘ , 2);
grid on
legend(toto , ‘location‘ , ‘SouthEast‘);
axis([-0.02 , 1.02 , -.02 , 1.02])
else
plot(fpmean , tpmean , fpfusion_mean , tpfusion_mean , ‘linewidth‘ , 2);
toto{nb_classifier*nb_descriptors+1} = [‘Fusion, AUC = ‘ , num2str(aucfusion_mean)];
grid on legend(toto , ‘location‘ , ‘SouthEast‘);
axis([-0.02 , 1.02 , -.02 , 1.02])
end
perf_topic_mean = reshape(mean(perf_topic , 2) , nb_topic , nb_classifier*nb_descriptors);
perf_topic_std = reshape(std(perf_topic , 0 , 2) , nb_topic , nb_classifier*nb_descriptors);
figure(3)
barh([prior , max(reshape(perf_topic_mean , nb_topic , nb_descriptors*nb_classifier) , [] , 2)])
h = legend(‘A priori‘ , ‘max‘ , ‘location‘ , ‘NorthEast‘);
set(h , ‘fontsize‘ , 9)
set(gca,‘YTick‘ ,1:nb_topic);
set(gca , ‘Yticklabel‘ , classe_name , ‘fontsize‘ , 11)
h = title(‘P_{d}‘);
set(h , ‘fontsize‘ , 13)
axis ij
efficiency = perf_topic_mean./ squeeze(prior(: , ones(1,nb_classifier) , ones(1,nb_descriptors )));
figure(4)
plot(1:nb_topic , efficiency , ‘linewidth‘ , 2)
set(gca,‘XTick‘ ,1:nb_topic);
set(gca,‘XTickLabel‘,classe_name);
aa=get(gca,‘XTickLabel‘);
bb=get(gca,‘XTick‘);
cc=get(gca,‘YTick‘);
th=text(bb,repmat(0-.3*(cc(2)-cc(1)),length(bb),1),aa,‘HorizontalAlignment‘,‘left‘,‘rotation‘,310);
set(th , ‘fontsize‘ , 7)
set(gca,‘XTickLabel‘,{});
h = title(‘Efficiency‘ );
set(h, ‘fontsize‘ , 13)
axis([0.5 , nb_topic+0.5 , 0.95 , 1.1*max(efficiency(:))])
h = legend(toto{1:end-1} , ‘location‘ , ‘SouthEast‘);
off = 4;
co = 1;
for d = 1:nb_descriptors
for c = 1:nb_classifier
figure(co + off)
sump = sum(matconf(: , : , : , c , d) , 1);
sump(sump==0) = 1;
probaR(: , : , c , d) = mean(matconf(: , : , : , c , d)./sump(ones(nb_topic , 1) , : , :) , 3);
confmatrix(probaR(: , : , c , d) , classe_name);
hold on
h = title(toto{co});
set(h , ‘fontsize‘ , 13)
hold off
co = co + 1;
end
end
probaR_mean = mean(mean(probaR,4),3);
figure(co + off)
confmatrix(probaR_mean , classe_name);该代码可对不同特征提取和分类方法进行设置，将相应变量取值设为自己需要的值即可。运行完就可得出各方法的准确率，选出准确率最高的，可进一步用于新数据识别。
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