HOG（Histograms of Oriented Gradients）用于特征分类
HOG（Histograms of Oriented Gradients）用于特征分类：视频链接：
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HOG特征提取算法的实现过程（图像特征提取）：
1）灰度化，如果是彩色图像，采用img = rgb2gray(img)灰度化；Gray = 0.3*R+0.59*G+0.11*B;
2）采用Gamma校正法对输入图像进行颜色空间的标准化（归一化）；目的是调节图像的对比度，降低图像局部的阴影和光照变化所造成的影响，同时可以抑制噪音的干扰；也可以简便地直接采用 img = sqrt(img);
3）计算图像每个像素的梯度（包括赋值和相位）；主要是为了捕获轮廓信息，相位保证图像信息的特征唯一性。
滤波掩膜如[-1,0,1]，具体看sobel、canny、prewitt等（MATLAB图像滤波去噪分析及其应用）
mag = sqrt( dx^2 + dy^2 )
phase = atan2(dy, dx)
dx为水平梯度，dy为垂直梯度；
4）对图像进行分块处理，每一个块是一个cell，每一个cell大小一般为16x16，cell水平、垂直移动步长为cell大小的一半，即水平、垂直步长为8，具体如下，rows和cols分别为图像高、宽；for i = 0: rows/8 - 2
for j= 0: cols/8 -2
mag_patch = mag(8*i+1 : 8*i+16 , 8*j+1 : 8*j+16);
ang_patch = phase(8*i+1 : 8*i+16 , 8*j+1 : 8*j+16);
end
end5）对每一个16x16的cell，进行8x8 blocks扫描，得到4个blocks，对这4个blocks进行不同梯度角度方向直方图求解：for x= 0:1
for y= 0:1
angleA =ang_patch(8*x+1:8*x+8, 8*y+1:8*y+8);
magA =mag_patch(8*x+1:8*x+8, 8*y+1:8*y+8);
end
end6）num_bins=9，对每一个block不同方向梯度进行直方图累加处理，具体如下：alpha= angleA(p,q);
% Binning Process (Bi-Linear Interpolation)
if alpha>=0 && alpha<=10
histr(1)=histr(1)+ magA(p,q)*(alpha+10)/20;
histr(9)=histr(9)+ magA(p,q)*(10-alpha)/20;
elseif alpha>10 && alpha<=30
histr(1)=histr(1)+ magA(p,q)*(30-alpha)/20;
histr(2)=histr(2)+ magA(p,q)*(alpha-10)/20;
elseif alpha>30 && alpha<=50
histr(2)=histr(2)+ magA(p,q)*(50-alpha)/20;
histr(3)=histr(3)+ magA(p,q)*(alpha-30)/20;
elseif alpha>50 && alpha<=70
histr(3)=histr(3)+ magA(p,q)*(70-alpha)/20;
histr(4)=histr(4)+ magA(p,q)*(alpha-50)/20;
elseif alpha>70 && alpha<=90
histr(4)=histr(4)+ magA(p,q)*(90-alpha)/20;
histr(5)=histr(5)+ magA(p,q)*(alpha-70)/20;
elseif alpha>90 && alpha<=110
histr(5)=histr(5)+ magA(p,q)*(110-alpha)/20;
histr(6)=histr(6)+ magA(p,q)*(alpha-90)/20;
elseif alpha>110 && alpha<=130
histr(6)=histr(6)+ magA(p,q)*(130-alpha)/20;
histr(7)=histr(7)+ magA(p,q)*(alpha-110)/20;
elseif alpha>130 && alpha<=150
histr(7)=histr(7)+ magA(p,q)*(150-alpha)/20;
histr(8)=histr(8)+ magA(p,q)*(alpha-130)/20;
elseif alpha>150 && alpha<=170
histr(8)=histr(8)+ magA(p,q)*(170-alpha)/20;
histr(9)=histr(9)+ magA(p,q)*(alpha-150)/20;
elseif alpha>170 && alpha<=180
histr(9)=histr(9)+ magA(p,q)*(190-alpha)/20;
histr(1)=histr(1)+ magA(p,q)*(alpha-170)/20;
end7）串联所有的cell特征，feature=;
8）归一化特征feature，feature=feature/sqrt(norm(feature)^2+0.001);
得到的feature特征为1xn的矩阵，区别于HoG目标探测，HoG目标探测，得到的feature为MxNxD的矩阵，其中，n = M*N*D；
主程序如下：% Feature Extraction
feat=hog_vector(im);
% Energy calculation
E=feat.^2;
Energy=(sum(E(:)))/(512*512);
%features set
features = ;
FeaturesHog(Index,:)=features;hog_vector函数如下：
function = hog_vector(im)
im=double(im);
rows=size(im,1);
cols=size(im,2);
Ix=im; %Basic Matrix assignment
Iy=im; %Basic Matrix assignment
% Gradients in X and Y direction. Iy is the gradient in X direction and Iy
% is the gradient in Y direction
for i=1:rows-2
Iy(i,:)=(im(i,:)-im(i+2,:));
end
for i=1:cols-2
Ix(:,i)=(im(:,i)-im(:,i+2));
end
gauss=fspecial('gaussian',8); %% Initialized a gaussian filter with sigma=0.5 * block width.
angle=atand(Ix./Iy); % Matrix containing the angles of each edge gradient
angle=imadd(angle,90); %Angles in range (0,180)
magnitude=sqrt(Ix.^2 + Iy.^2);
% figure,imshow(uint8(angle));
% figure,imshow(uint8(magnitude));
% Remove redundant pixels in an image.
angle(isnan(angle))=0;
magnitude(isnan(magnitude))=0;
feature=[]; %initialized the feature vector
% Iterations for Blocks
for i = 0: rows/8 - 2
for j= 0: cols/8 -2
%disp()
mag_patch = magnitude(8*i+1 : 8*i+16 , 8*j+1 : 8*j+16);
%mag_patch = imfilter(mag_patch,gauss);
ang_patch = angle(8*i+1 : 8*i+16 , 8*j+1 : 8*j+16);
block_feature=[];
%Iterations for cells in a block
for x= 0:1
for y= 0:1
angleA =ang_patch(8*x+1:8*x+8, 8*y+1:8*y+8);
magA =mag_patch(8*x+1:8*x+8, 8*y+1:8*y+8);
histr=zeros(1,9);
%Iterations for pixels in one cell
for p=1:8
for q=1:8
%
alpha= angleA(p,q);
% Binning Process (Bi-Linear Interpolation)
if alpha>=0 && alpha<=10
histr(1)=histr(1)+ magA(p,q)*(alpha+10)/20;
histr(9)=histr(9)+ magA(p,q)*(10-alpha)/20;
elseif alpha>10 && alpha<=30
histr(1)=histr(1)+ magA(p,q)*(30-alpha)/20;
histr(2)=histr(2)+ magA(p,q)*(alpha-10)/20;
elseif alpha>30 && alpha<=50
histr(2)=histr(2)+ magA(p,q)*(50-alpha)/20;
histr(3)=histr(3)+ magA(p,q)*(alpha-30)/20;
elseif alpha>50 && alpha<=70
histr(3)=histr(3)+ magA(p,q)*(70-alpha)/20;
histr(4)=histr(4)+ magA(p,q)*(alpha-50)/20;
elseif alpha>70 && alpha<=90
histr(4)=histr(4)+ magA(p,q)*(90-alpha)/20;
histr(5)=histr(5)+ magA(p,q)*(alpha-70)/20;
elseif alpha>90 && alpha<=110
histr(5)=histr(5)+ magA(p,q)*(110-alpha)/20;
histr(6)=histr(6)+ magA(p,q)*(alpha-90)/20;
elseif alpha>110 && alpha<=130
histr(6)=histr(6)+ magA(p,q)*(130-alpha)/20;
histr(7)=histr(7)+ magA(p,q)*(alpha-110)/20;
elseif alpha>130 && alpha<=150
histr(7)=histr(7)+ magA(p,q)*(150-alpha)/20;
histr(8)=histr(8)+ magA(p,q)*(alpha-130)/20;
elseif alpha>150 && alpha<=170
histr(8)=histr(8)+ magA(p,q)*(170-alpha)/20;
histr(9)=histr(9)+ magA(p,q)*(alpha-150)/20;
elseif alpha>170 && alpha<=180
histr(9)=histr(9)+ magA(p,q)*(190-alpha)/20;
histr(1)=histr(1)+ magA(p,q)*(alpha-170)/20;
end
end
end
block_feature=; % Concatenation of Four histograms to form one block feature
end
end
% Normalize the values in the block using L1-Norm
block_feature=block_feature/sqrt(norm(block_feature)^2+0.01);
feature=; %Features concatenation
end
end
feature(isnan(feature))=0; % Removing Infinitiy values
% Normalization of the feature vector using L2-Norm
feature=feature/sqrt(norm(feature)^2+0.001);
for z=1:length(feature)
if feature(z)>0.2
feature(z)=0.2;
end
end
feature=feature/sqrt(norm(feature)^2+0.001);
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