BDIP-BVLC纹理

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BDIP&BVLC介绍

BDIP（block difference of inverse probabilities），基于块的逆转概率（私以为很拗口，翻译不佳），描述了在一个M*M大小的块内，像素值变化的快慢，计算的BDIP值越大，原图像像素值变化越剧烈。
BVLC(block-based of variation of local correlation coefficients)，基于块的局部相关系数，描述了一个像素点与周围四个方向（-90， 0， -45， 45）的相关度，计算的BVLC值越大，原图像越粗糙。
BDIP&BVLC特征图的特征描述子可见文末附带文献。

BDIP&BVLC计算方法

BDIP

I(i, j)表示属于M*M块中某一个像素值

BVLC

k，l分别表示水平和垂直移动，I(i, j)表示属于M*M块中某一个像素值，O（4）表示四个平移方向
σ（k，l）表示在平移后M*M像素块里的标准差，σ（0，0）表示当前M*M块的标准差
μ（k，l）表示在平移后M*M像素块里的均值，μ（0，0）表示当前M*M块的均值
像素平移示意图

实现

（1）BDIP

void bdip(const Mat& src){    Mat bdip;    bdip = src.clone();    int nRows;    int nCols;    nRows = src.rows;    nCols = src.cols;    //计算bdip特征图    for(int r = 0; r < nRows; ++r)    {        for(int c = 0; c < nCols; ++c)        {            float blockValue = 0.0;            int blockSum = 0;            int blockMax = 0;            //边缘不做考虑            if(0 == r || nRows - 1 == r ||                0 == c || nCols - 1 == c)            {                blockValue = 0;            }            else            {                uchar v1, v2, v3, v4;                v1 = src.ptr<uchar>(r)[c];                v2 = src.ptr<uchar>(r)[c + 1];                v3 = src.ptr<uchar>(r + 1)[c];                v4 = src.ptr<uchar>(r + 1)[c + 1];                blockSum = v1 + v2 + v3 + v4;                blockMax = MAX(MAX(v1, v2), MAX(v3, v4));                blockValue = 4 - blockSum*1.0/blockMax;            }            bdip.ptr<uchar>(r)[c] = 64*blockValue;        }    }#if 1    imshow("bdip", bdip);    cvWaitKey(0);#endif    return;}

（2）BVLC

struct  SHIFTDIRECTION{    int x;    int y;};const SHIFTDIRECTION shift[5] = {0, 0, 1, 0, 0, 1, 1, 1, 0, 1};void bvlc(const Mat& src){    int nRows;    int nCols;    nRows = src.rows;    nCols = src.cols;    //填充边界    Mat tmp;    copyMakeBorder(src, tmp, 2, 2, 2, 2,BORDER_REFLECT);    tmp.convertTo(tmp, CV_32FC1, 1.0, 0);    Mat bvlc;    bvlc = src.clone();    //计算blvc特征图    for(int r = 2; r < nRows + 2; ++r)    {        for(int c = 2; c < nCols + 2; ++c)        {            float variance[5] = {0.0};            float mean[5] = {0.0};            float value[4] = {0,0};            for(int i = 0; i < 5; ++i)            {                int curR = r + shift[i].x;                int curC = c + shift[i].y;                //计算平移后四个像素均值、方差                if(i < 4)                {                    mean[i] =                         tmp.ptr<float>(curR)[curC] +                         tmp.ptr<float>(curR + 1)[curC] +                        tmp.ptr<float>(curR)[curC + 1] +                         tmp.ptr<float>(curR + 1)[curC + 1];                    mean[i] /= 4;                    variance[i] =                         pow((tmp.ptr<float>(curR)[curC] - mean[i]), 2) +                         pow((tmp.ptr<float>(curR + 1)[curC] - mean[i]), 2) +                         pow((tmp.ptr<float>(curR)[curC + 1] - mean[i]), 2) +                         pow((tmp.ptr<float>(curR + 1)[curC + 1] - mean[i]), 2);                }                //最后一个块的位置方向与前三块有区别                else                {                    mean[i] =                         tmp.ptr<float>(curR)[curC] +                         tmp.ptr<float>(curR - 1)[curC] +                        tmp.ptr<float>(curR)[curC + 1] +                         tmp.ptr<float>(curR - 1)[curC + 1];                      mean[i] /= 4;                    variance[i] =                         pow((tmp.ptr<float>(curR)[curC] - mean[i]), 2) +                         pow((tmp.ptr<float>(curR - 1)[curC] - mean[i]), 2) +                         pow((tmp.ptr<float>(curR)[curC + 1] - mean[i]), 2) +                         pow((tmp.ptr<float>(curR - 1)[curC + 1] - mean[i]), 2);                }                variance[i] /= 4;                variance[i] = sqrt(variance[i]);            }               //计算四个方向块与原块的相关系数value[i]            for(int i = 1; i < 5; ++i)            {                   value[i - 1] =                     tmp.ptr<float>(r)[c]*tmp.ptr<float>(r + shift[i].x)[c +shift[i].y] - mean[0]*mean[i] +                    tmp.ptr<float>(r + 1)[c]*tmp.ptr<float>(r + shift[i].x)[c +shift[i].y] - mean[0]*mean[i] +                    tmp.ptr<float>(r)[c + 1]*tmp.ptr<float>(r + shift[i].x)[c +shift[i].y] - mean[0]*mean[i] +                    tmp.ptr<float>(r + 1)[c + 1]*tmp.ptr<float>(r + shift[i].x)[c +shift[i].y] - mean[0]*mean[i];                value[i - 1] /= 4;                value[i - 1] /= (variance[0]*variance[i]);            }            //获取最大和最小相关系数            float max = value[0];            float min = value[0];            for(int i = 0; i < 4; ++i)            {                if(value[i] > max)                {                    max = value[i];                }                if(value[i] < min)                {                    min = value[i];                }            }            //计算BLVC数值            bvlc.ptr<uchar>(r - 2)[c - 2] = max - min;        }    }#if 1    imshow("bvlc", bvlc);    waitKey(0);#endif    return;}

结果

原始图像：
BDIP&BVLC：

引用

Image retrieval using BDIP and BVLC moments

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