lbp特征的matlab实现_matlab lbp函数

matlab自带函数extractLBPFeatures可以很好地提取lbp特征，但是不好进行改进，因此在网上找了人家用maltab实现的lbp特征提取，但是性能比自带函数稍差，代码主要来自
http://www.cse.oulu.fi/wsgi/MVG/Downloads/LBPMatlab
针对lbp特征提取部分，有稍微进行修改，代码如下：
testmylbp.m

%lbp的参数是以下，直接在这改，就不用在函数里一个一个的改动了
lbpcellsize=48;
mapping=getmapping(8,'u2');%以统一模式lbp去映射 
radius=1;
neighbors=8;
mode='L2';%h仅仅只是直方图，没有归一化，‘nh’是L1归一化,'L2'是L2归一化

image=imread('rice.png');
imageSize=[256 256];
image = imresize(image,imageSize); %缩放
 % LBP
    [row,col,m]=size(image);
    if m>1
        image=rgb2gray(image);
    end   
    
lbpfeature=mylbp(image,row,col,lbpcellsize,lbpcellsize,radius,neighbors,mapping,mode);
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mylbp.m(可以针对子块提取lbp特征，需要调用lbp函数)

function H2=mylbp(img,row,col,blocksizerow,blocksizecol,radius,neighbors,mapping,mode)
% [row,col,k]=size(img);
% if  k>1
%      img=rgb2gray(img);
% end
new_row = floor(row/blocksizerow) * blocksizerow;%ceil是向上取整,floor向下
new_col = floor(col/blocksizecol) * blocksizecol;
new_img = imresize(img, [new_row new_col], 'bilinear');%以双线性插值来缩放

[y_row y_col dim] = size(new_img);
row_blk_num = y_row/blocksizerow;  % 3
col_blk_num = y_col/blocksizecol;  % 6

blocks = 1;
for i = 1:row_blk_num
    for j = 1:col_blk_num
%         disp(blocks);
        block = new_img((i - 1) * blocksizerow + 1 : i * blocksizerow, (j - 1) * blocksizecol + 1 : j * blocksizecol, :);
        H1(blocks,:)=lbp(block,radius,neighbors,mapping,mode);
        
%         imshow(block);
        %imwrite(block, ['./' num2str(blocks) '.jpg']);
        blocks = blocks + 1;
    end
end
H2=reshape(H1,1,[]);%变成行向量
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lbp.m 代码如下

%LBP returns the local binary pattern image or LBP histogram of an image.
%  J = LBP(I,R,N,MAPPING,MODE) returns either a local binary pattern
%  coded image or the local binary pattern histogram of an intensity
%  image I. The LBP codes are computed using N sampling points on a 
%  circle of radius R and using mapping table defined by MAPPING.
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