【特征提取】MultiBlock-LBP特征

MB-LBP特征，注意区分是Multi-block LBP还是MultiScale Block LBP,区别是是否使用了多尺度方法。其中Multiscale Block LBP，来源于论文，中科院的人发明的，opencv源码在Traincascade级联目标训练检测中的LBP特征使用的就是MultiScale Block LBP。 

 generateFeatures

void CvLBPEvaluator::generateFeatures()
{
    int offset = winSize.width + 1;
    for( int x = 0; x < winSize.width; x++ )
        for( int y = 0; y < winSize.height; y++ )
            for( int w = 1; w <= winSize.width / 3; w++ )
                for( int h = 1; h <= winSize.height / 3; h++ )
                    if ( (x+3*w <= winSize.width) && (y+3*h <= winSize.height) )
                        features.push_back( Feature(offset, x, y, w, h ) );
    numFeatures = (int)features.size();
}

Feature

CvLBPEvaluator::Feature::Feature( int offset, int x, int y, int _blockWidth, int _blockHeight )
{
    Rect tr = rect = cvRect(x, y, _blockWidth, _blockHeight);
    CV_SUM_OFFSETS( p[0], p[1], p[4], p[5], tr, offset )
    tr.x += 2*rect.width;
    CV_SUM_OFFSETS( p[2], p[3], p[6], p[7], tr, offset )
    tr.y +=2*rect.height;
    CV_SUM_OFFSETS( p[10], p[11], p[14], p[15], tr, offset )
    tr.x -= 2*rect.width;
    CV_SUM_OFFSETS( p[8], p[9], p[12], p[13], tr, offset )
}

calc

inline uchar CvLBPEvaluator::Feature::calc(const cv::Mat &_sum, size_t y) const
{
    const int* psum = _sum.ptr<int>((int)y);
    int cval = psum[p[5]] - psum[p[6]] - psum[p[9]] + psum[p[10]];

    return (uchar)((psum[p[0]] - psum[p[1]] - psum[p[4]] + psum[p[5]] >= cval ? 128 : 0) |   // 0
        (psum[p[1]] - psum[p[2]] - psum[p[5]] + psum[p[6]] >= cval ? 64 : 0) |    // 1
        (psum[p[2]] - psum[p[3]] - psum[p[6]] + psum[p[7]] >= cval ? 32 : 0) |    // 2
        (psum[p[6]] - psum[p[7]] - psum[p[10]] + psum[p[11]] >= cval ? 16 : 0) |  // 5
        (psum[p[10]] - psum[p[11]] - psum[p[14]] + psum[p[15]] >= cval ? 8 : 0) | // 8
        (psum[p[9]] - psum[p[10]] - psum[p[13]] + psum[p[14]] >= cval ? 4 : 0) |  // 7
        (psum[p[8]] - psum[p[9]] - psum[p[12]] + psum[p[13]] >= cval ? 2 : 0) |   // 6
        (psum[p[4]] - psum[p[5]] - psum[p[8]] + psum[p[9]] >= cval ? 1 : 0));     // 3
}

CV_SUM_OFFSETS

#define CV_SUM_OFFSETS( p0, p1, p2, p3, rect, step )                      
    /* (x, y) */                                                          
    (p0) = (rect).x + (step) * (rect).y;                                  
    /* (x + w, y) */                                                      
    (p1) = (rect).x + (rect).width + (step) * (rect).y;                   
    /* (x, y + h) */                                    
    (p2) = (rect).x + (step) * ((rect).y + (rect).height);                
    /* (x + w, y + h) */                                                  
    (p3) = (rect).x + (rect).width + (step) * ((rect).y + (rect).height);

参考

1. LBP特征原理及代码实现；

2. opencv源码解析之LBP特征；

3. 机器学习之LBP特征；

4. matalb code之MB-LBP特征；

5. Face Detection Based on Multi-Block LBP Representation;

6. Learning Multi-scale Block Local Binary Patterns for Face Recognition;

完