京東金融大數據競賽豬臉識別（3）-方法選取
阿新 • • 發佈：2018-03-13
特征提取 圖像識別 分類方法 由於可用的特征提取和分類器構造方法較多，為了確定選取方法。現在在視頻提取的圖像上進行算法驗證，取這些圖像的一部分進行訓練，剩余部分進行測試，並進行交叉驗證。找到準確率最高的方法後再將其用於測試集。
以reco_toolbox為例，下面給出方法選取的代碼：
以reco_toolbox為例，下面給出方法選取的代碼：
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; % 交叉驗證折數 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);
該代碼可對不同特征提取和分類方法進行設置，將相應變量取值設為自己需要的值即可。運行完就可得出各方法的準確率，選出準確率最高的，可進一步用於新數據識別。
京東金融大數據競賽豬臉識別（3）-方法選取