图像特征提取之（一）HOG特征（理论知识）

计算图像横坐标和纵坐标方向的梯度，并据此计算每个像素位置的梯度方向值；求导操作不仅能够捕获轮廓，人影和一些纹理信息，还能进一步弱化光照的影响。

图像中像素点(x,y)的梯度为：

</pre><pre code_snippet_id="475996" snippet_file_name="blog_20140926_1_2230163" name="code" class="plain">function HOGFeature = ImgHOGFeature( imgPathName, cell_size, nblock,...,    overlap, angle, bin_num)% 计算输入图像的特征描述子% imgPathName：图片路径% cell_size: cell的长宽% nblock: block的width、height包括的cell的个数% overlap: block重叠的比例% angle: 180\360% bin_num: 方向bin的数目 if nargin<2    % default parameter    cell_size=8;    nblock=2;    overlap=0.5;    angle=180;    bin_num=9;elseif nargin<6    error('Input parameters are not enough.');end Img = imread(imgPathName);if size(Img,3) == 3    % 简化计算，直接转换成灰度图像    G = rgb2gray(Img);else    G = Img;end [height, width] = size(G); % 计算x、y方向的梯度hx = [-1,0,1];hy = -hx';grad_x = imfilter(double(G),hx);grad_y = imfilter(double(G),hy); % 计算梯度的模长grad_mag=sqrt(grad_x.^2+grad_y.^2); % 计算梯度的方向index= grad_x==0;grad_x(index)=1e-5;YX=grad_y./grad_x;if angle==180    grad_angle= ((atan(YX)+(pi/2))*180)./pi; elseif angle==360    grad_angle= ((atan2(grad_y,grad_x)+pi).*180)./pi;end % orient binbin_angle=angle/bin_num;grad_orient=ceil(grad_angle./bin_angle); % 计算block的个数block_size=cell_size*nblock;skip_step=block_size*overlap;x_step_num=floor((width-block_size)/skip_step+1);y_step_num=floor((height-block_size)/skip_step+1); % 初始化hog特征描述子feat_dim=bin_num*nblock^2;HOGFeature=zeros(feat_dim,x_step_num*y_step_num); for k=1:y_step_num    for j=1:x_step_num        % block的左上角坐标        x_off = (j-1)*skip_step+1;        y_off = (k-1)*skip_step+1;         % 取得block的梯度大小和方向        b_mag=grad_mag(y_off:y_off+block_size-1,x_off:x_off+block_size-1);        b_orient=grad_orient(y_off:y_off+block_size-1,x_off:x_off+block_size-1);         % 当前block的hog直方图        currFeat = BinHOGFeature(b_mag, b_orient, cell_size,nblock, bin_num, false);        HOGFeature(:, (k-1)*x_step_num+j) = currFeat;     endend endfunction blockfeat = BinHOGFeature( b_mag,b_orient,cell_size,nblock,...    bin_num, weight_vote)% 计算1个block的hog% weight_vote: 是否进行高斯加权投票 % block的HOG直方图blockfeat=zeros(bin_num*nblock^2,1); % 高斯权重gaussian_weight=fspecial('gaussian',cell_size*nblock,0.5*cell_size*nblock); % 分割blockfor n=1:nblock    for m=1:nblock        % cell的左上角坐标        x_off = (m-1)*cell_size+1;        y_off = (n-1)*cell_size+1;         % cell的梯度大小和方向        c_mag=b_mag(y_off:y_off+cell_size-1,x_off:x_off+cell_size-1);        c_orient=b_orient(y_off:y_off+cell_size-1,x_off:x_off+cell_size-1);         % cell的hog直方图        c_feat=zeros(bin_num,1);        for i=1:bin_num            % 是否进行高斯加权 投票            if weight_vote==false                c_feat(i)=sum(c_mag(c_orient==i));            else                c_feat(i)=sum(c_mag(c_orient==i).*gaussian_weight(c_orient==i));            end        end         % 合并到block的HOG直方图中        count=(n-1)*nblock+m;        blockfeat((count-1)*bin_num+1:count*bin_num,1)=c_feat;    endend % 归一化 L2-normsump=sum(blockfeat.^2);blockfeat = blockfeat./sqrt(sump+eps^2);