图像识别进入深度学习时代后，特征提取这个词的使用频率明显下降了。因为深度网络已经完成了从图像输入到分类结果输出的全过程，似乎不需要再关心特征的好坏和特征提取对于识别结果的影响。不过，不管从算法研究还是工程实现角度看，将特征提取独立出来应该更有利。这样我们可以对各种特征提取方法和各种识别算法进行组合，找出效果最好的方案。

%exam1.m extract features from every imageclc,close all, clear all,drawnowdatabase_name        = {'JDTest'};database_ext         = {'jpg' , 'jpg' , 'png'};descriptors_name     = {'mlhoee_spyr' , 'mlhmslsd_spyr' , 'mlhmslbp_spyr' , 'mlhmsldp_spyr'};choice_database      = [1]; choice_descriptors   = [3]; %mlhoee_spyr=1/mlhmslsd_spyr=2/mlhmslbp_spyr=3/mlhmsldp_spyr=4do_extract_feature   = 1;   %no=0/yes=1data_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');des_dir              = fullfile(pwd , 'descriptors');addpath(core_dir);%执行描述符配置脚本文件eval([data_name , '_config_descriptors']);descriptors_param    = cell(1 , length(descriptors_name));descriptors_param{1} = mlhoee;descriptors_param{2} = mlhmslsd;descriptors_param{3} = mlhmslbp;descriptors_param{4} = mlhmsldp;descriptors_size     = cell(1 , length(descriptors_name));descriptors_size{1}  = descriptors_param{1}{1}.size;descriptors_size{2}  = descriptors_param{2}{1}.size;descriptors_size{3}  = descriptors_param{3}{1}.size;descriptors_size{4}  = descriptors_param{4}{1}.size;nb_descriptors       = length(choice_descriptors);if(do_extract_feature)        for j  = 1 : nb_descriptors        current_path  = fullfile(pwd , 'images' , data_name );        current_dir     = dir(fullfile(current_path , ['*.' , im_ext]));            m                    = length(current_dir);        current_descriptor = choice_descriptors(j);        current_size           = descriptors_size{current_descriptor};        base_descriptor     = descriptors_name{current_descriptor};                   eval(['descriptors   = @' base_descriptor ';']);        %初始化特征存储变量        X                            = zeros(descriptors_size{current_descriptor} , m);              for i = 1 : m            I                  = imread(fullfile(current_path , current_dir(i).name));            [h,w]           = size(I);                        %将图像缩小一半            J                 = imresize(I,[h/2,w/2]);            X(:,i)            = descriptors(J , descriptors_param{current_descriptor}{:});                                   fprintf('descriptor = %s, image = %s (%d/%d)\n' , base_descriptor ,  current_dir(i).name , i , m)            drawnow        end                %将图像特征存储为文件        save(fullfile(feat_dir , [data_name , '_' , base_descriptor]) , 'X'  )        clear X ;            end    fprintf(‘Feature extraction  finished!');end

运行该程序就可以对image文件夹下JDTest文件夹内各子文件夹内的图像提取特征并存为文件。

%exam2.m extract bag-of-features for every imageclc,close all, clear ,drawnowdatabase_name        = {'JDPig' };database_ext         = {'jpg' , 'jpg' , 'png'};descriptors_name     = {'denseSIFT' , 'denseCOLOR' , 'densePATCH' , 'denseMBLBP' , 'denseMBLDP'};encoding_name        = {'yael_kmeans' , 'mexTrainDL'};features_name          = {'mlhbow_spyr' , 'dl_spyr' , 'mlhlcc_spyr'};choice_database      = [1]; choice_descriptors   = [1]; choice_encoding      = [2]; %Kmeans=1/Sparse Learning =2choice_feature          = [2]; %SP-Histogram = 1/Sparse Pooling = 2/LCC = 3do_extract_patches     = 1;   %no=0/yes=1do_encoding                = 1;   %no=0/yes=1do_compute_features  = 1;   %no=0/yes=1dicoshared                   = 0;   %no=0/yes=1data_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');dico_dir                  = fullfile(pwd , 'dico');des_dir                   = fullfile(pwd , 'descriptors');addpath(core_dir)dirim                       = dir(images_dir);nb_topic                 = length(dirim) - 2;classe_name          = cellstr(char(dirim(3:nb_topic+2).name))';%执行相关配置文件eval([data_name , '_config_descriptors']);eval([data_name , '_config_encoding']);eval([data_name , '_config_features']);nbimagespertopic     = zeros(1 , nb_topic);for i = 1:nb_topic    dir_name            = dir(fullfile(pwd  , 'images' , data_name , dirim(i+2).name , ['*.' , im_ext]));    nbimagespertopic(i) = length(dir_name);endN                    = sum(nbimagespertopic);descriptors_param    = cell(1 , length(descriptors_name));descriptors_param{1} = sift;descriptors_param{2} = color;descriptors_param{3} = patch;descriptors_param{4} = mblbp;descriptors_param{5} = mbldp;descriptors_size     = cell(1 , length(descriptors_name));descriptors_size{1}  = descriptors_param{1}{1}.size;descriptors_size{2}  = descriptors_param{2}{1}.size;descriptors_size{3}  = descriptors_param{3}{1}.size;descriptors_size{4}  = descriptors_param{4}{1}.size;descriptors_size{5}  = descriptors_param{5}{1}.size;encoding_param       = cell(1 , length(encoding_name));encoding_param{1}    = yael;encoding_param{2}    = spams;features_param        = cell(1 , length(features_name));features_param{1}    = mlhbow_feat;features_param{2}    = dl_feat;features_param{3}    = mlhlcc_feat;nb_descriptors       = length(choice_descriptors);nb_encoding          = length(choice_encoding);nb_features          = length(choice_encoding);current_descriptor = choice_descriptors;base_descriptor    = descriptors_name{current_descriptor};featfile = fullfile(feat_dir , [data_name , '_' , base_descriptor]);featfile = [featfile,'.mat'];if (~exist(featfile))    do_extract_patches = 1;else    do_extract_patches = 0;endif(do_extract_patches)    for j  = 1 : nb_descriptors        current_descriptor  = choice_descriptors(j);        current_size           = descriptors_size{current_descriptor};        base_descriptor     = descriptors_name{current_descriptor};        nbpatches              = descriptors_param{current_descriptor}{1}.nbpatches;        nbpatchetotal         = nbpatches*N;        standardize            = descriptors_param{current_descriptor}{1}.standardize;        whithning               = descriptors_param{current_descriptor}{1}.whithning;        patchdim                = descriptors_param{current_descriptor}{1}.patchdim;        eval(['descriptors = @' base_descriptor ';']);        fprintf('descriptor = %s \n\n' , base_descriptor)        drawnow        X                  = zeros(descriptors_size{current_descriptor} , N , 'single');        Z                  = zeros(nbpatches*6 , N , 'single');        S                  = zeros(2 , N , 'uint16');        Y                  = zeros(nbpatches , N , 'single');        y                  = zeros(1 , N );        co                 = 1;        %对每类图像进行处理        for t = 1 : nb_topic                        current_path  = fullfile(pwd , 'images' , data_name , dirim(t+2).name);            current_dir   = dir(fullfile(current_path , ['*.' , im_ext]));            current_topic = char(classe_name(t));                     for i = 1 : length(current_dir)                                I                   = imread(fullfile(current_path , current_dir(i).name));                                fprintf('descriptor = %s, topic = %s (%d/%d), image = %s (%d/%d)\n' , base_descriptor , current_topic, t , nb_topic , current_dir(i).name , i , nbimagespertopic(t))                drawnow                                [des , fea]         = descriptors(I , descriptors_param{current_descriptor}{:});                                X(: , co)            = reshape(single(des) , current_size , 1);                Z(: , co)            = reshape(single(fea(1:6,:)) , nbpatches*6 , 1);                Y(: , co)            = t*ones(nbpatches , 1);                y(co)                 = t;                co                     = co + 1;            end        end                 if(dicoshared)            Z(3:4 , :)                                                                   = 1;            descriptors_param{current_descriptor}{1}.scale     = 1;            descriptors_param{current_descriptor}{1}.nbscale  = 1;            descriptors_param{current_descriptor}{1}.dimcolor = 1;            features_param{current_features}.scale                  = 1;        end        X                           = reshape(X , patchdim , nbpatchetotal);        Z                           = reshape(Z , [6 , nbpatchetotal]);        Y                           = reshape(Y , 1 , nbpatchetotal);        if(standardize)            fprintf('Standardize patches\n' )            drawnow            mX            = mean(X , 2);            stdX          = std(X , 0 , 2);            stdX(stdX==0) = 1;            X             = (X - mX(: , ones(1 , size(X , 2))))./stdX(: , ones(1 , size(X , 2)));                        fprintf('End Standardize\n' )            drawnow        end        if(whithning)                        fprintf('Whithning patches\n' )            drawnow                       covX    = (1/(size(X,2)-1))*(X*X');            [V,D]   = eig(covX);            T       = (V*diag(sqrt(1 ./(diag(D) + 0.1))))*V';            X       = T * X;                        fprintf('End whithning\n' )            drawnow                    end                fprintf('Saving patches descriptor  %s ...\n' , [data_name , '_' , base_descriptor]);        drawnow         save(fullfile(des_dir , [data_name , '_' , base_descriptor]) , 'X' , 'Z'  , 'Y' , 'y' , 'classe_name' , 'patchdim' , 'nbpatches' , 'N'  , '-v7.3')        clear X Z Y y;    endendcurrent_descriptor = choice_descriptors;base_descriptor    = descriptors_name{current_descriptor};current_encoding = choice_encodingbase_encoding    = encoding_name{current_encoding};dictfile = fullfile(dico_dir , [data_name , '_' , base_encoding , '_' , base_descriptor]);dictfile = [dictfile,'.mat'];if (~exist(featfile))    do_encoding = 1;else    do_encoding = 0;endif(do_encoding)    for j  = 1 : nb_encoding         current_encoding = choice_encoding(j);        base_encoding    = encoding_name{current_encoding};        eval(['encoding = @' base_encoding ';']);        for i  = 1 : nb_descriptors                        current_descriptor = choice_descriptors(i);            base_descriptor    = descriptors_name{current_descriptor};                        nbpatches            = descriptors_param{current_descriptor}{1}.nbpatches;            nbpatchetotal       = nbpatches*N;            patchdim              = descriptors_param{current_descriptor}{1}.patchdim;            nbscale               = descriptors_param{current_descriptor}{1}.nbscale;            dimcolor              = descriptors_param{current_descriptor}{1}.dimcolor;            K                         = encoding_param{current_encoding}{current_descriptor}.K;            nbpatchesperclass  = encoding_param{current_encoding}{current_descriptor}.nbpatchesperclass;            D                          = zeros(patchdim , K , nbscale , dimcolor , 'single');                        fprintf('Loading patches descriptor %s ...\n' , [data_name , '_' , base_descriptor]);            drawnow                        load(fullfile(des_dir , [data_name , '_' , base_descriptor]) , 'X' , 'Z' , 'Y' , 'y' , 'classe_name')            for c = 1 : dimcolor                for s = 1 : nbscale                    currentscale      = descriptors_param{current_descriptor}{1}.scale(s);                    index                 = find( (Z(3 , :) == currentscale ) & (Z(4 , :) == c) );                    Yindex               = Y(index);                    lindex                 = length(index);                    indexdico            = [];                    for t = 1 : nb_topic                        indt          = find(Yindex == t);                        lindt         = length(indt);                        idx           = randperm(lindt);                        idxtemp   = idx(1:min(nbpatchesperclass ,lindt));                        indexdico = [indexdico , index(indt(idxtemp))];                    end                    fprintf('Learning dictionary of K = %d words with encoder = %s, scale = %5.3f, dimcolor = %d from %d/%d patches of %s \n' , K , base_encoding , currentscale , c , length(indexdico) , lindex , base_descriptor)                    drawnow                    D(: , : , s , c)  = encoding(X(: , indexdico) , encoding_param{current_encoding}{current_descriptor});                end            end                        fprintf('Saving dictionary  %s ...\n' , [data_name , '_' , base_encoding , '_' , base_descriptor]);            drawnow                        save(fullfile(dico_dir , [data_name , '_' , base_encoding , '_' , base_descriptor]) , 'D');            clear X Z Y y classe_name;                    end    endendcurrent_descriptor = choice_descriptors;base_descriptor    = descriptors_name{current_descriptor};current_features   = choice_feature;base_features      = features_name{current_features};dlfeatfile = fullfile(feat_dir , [data_name , '_' , base_descriptor , '_' , base_features]);dlfeatfile = [dlfeatfile,'.mat'];if (~exist(dlfeatfile))    do_compute_features = 1;else    do_compute_features = 0;endif(do_compute_features)    for j  = 1 : nb_encoding        current_features   = choice_feature(j);        base_features      = features_name{current_features};        current_encoding   = choice_encoding(j);        base_encoding      = encoding_name{current_encoding};        eval(['features = @' base_features ';']);        for i  = 1 : nb_descriptors            current_descriptor                                          = choice_descriptors(i);            base_descriptor                                             = descriptors_name{current_descriptor};            nbpatches                                                      = descriptors_param{current_descriptor}{1}.nbpatches;            nbpatchetotal                                               = nbpatches*N;            patchdim                                                      = descriptors_param{current_descriptor}{1}.patchdim;            nbscale                                                        = descriptors_param{current_descriptor}{1}.nbscale;            dimcolor                                                       = descriptors_param{current_descriptor}{1}.dimcolor;            features_param{current_features}{current_descriptor}.scale  = descriptors_param{current_descriptor}{1}.scale;            features_param{current_features}{current_descriptor}.L      = patchdim;                        current_feature_param                                       = features_param{current_features}{current_descriptor};                        fprintf('Loading dictionnary %s ...\n' , [data_name , '_' , base_encoding , '_' , base_descriptor]);            drawnow            load(fullfile(dico_dir , [data_name , '_' , base_encoding , '_' , base_descriptor]) , 'D');                        fprintf('Loading patches descriptor %s ...\n' , [data_name , '_' , base_descriptor]);            drawnow                        load(fullfile(des_dir , [data_name , '_' , base_descriptor]) , 'X' , 'Z'  , 'y' ,  'classe_name');                        K                                                           = size(D , 2);            nH                                                          = current_feature_param.nH;            X                                                           = reshape(X , descriptors_size{current_descriptor} , N);            Z                                                           = reshape(Z , 6*nbpatches , N);            F                                                           = zeros(K*nH*nbscale*dimcolor , N);            co                                                          = 1;            for t = 1 : nb_topic                current_path  = fullfile(pwd , 'images' , data_name , dirim(t+2).name);                current_dir   = dir(fullfile(current_path , ['*.' , im_ext]));                current_topic = char(classe_name(t));                for i = 1 : length(current_dir)                    fprintf('encoder = %s, topic = %s (%d/%d), patches = %s , image = %s (%d/%d)\n' , base_features , current_topic, t , nb_topic , base_descriptor , current_dir(i).name , i , nbimagespertopic(t))                    drawnow                                        XX              = reshape(X(: , co) , patchdim , nbpatches);                    ZZ              = reshape(Z(: , co) , 6 , nbpatches);                    F(: , co)       = features(D , XX , ZZ  , current_feature_param);                    co              = co + 1;                end            end                        X                  = F;            clear F Z S;            fprintf('Saving features  %s ...\n' , [data_name , '_' , base_descriptor , '_' , base_features]);            drawnow            dlfeatfile = fullfile(feat_dir , [data_name , '_' , base_descriptor , '_' , base_features]);            save( dlfeatfile, 'X' , 'y' , 'classe_name' , '-v7.3');        end    endendfprintf('Feature extraction  finished!!');

该程序提取提取后对特征聚类生成词袋，并依据词袋对图像特征进行编码，为每幅图像生成特征。详细内容可参看。