%exam1.m extract features from every image
clc,close all, clear all,drawnow
database_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=4
do_extract_feature = 1; %no=0/yes=1
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‘);
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 image
clc,close all, clear ,drawnow
database_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 =2
choice_feature = [2]; %SP-Histogram = 1/Sparse Pooling = 2/LCC = 3
do_extract_patches = 1; %no=0/yes=1
do_encoding = 1; %no=0/yes=1
do_compute_features = 1; %no=0/yes=1
dicoshared = 0; %no=0/yes=1
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‘);
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);
end
N = 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;
end
if(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;
end
end
current_descriptor = choice_descriptors;
base_descriptor = descriptors_name{current_descriptor};
current_encoding = choice_encoding
base_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;
end
if(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
end
end
current_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;
end
if(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
end
end
fprintf(‘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Matlab图像识别/检索系列(9)—开源工具介绍之图像识别reco_toolbox。
原文：http://blog.51cto.com/8764888/2085964