C++版的LLC代码

图像稀疏编码总结：LLC和SCSPM ，文章对稀疏编码讲解非常详细。

《Locality-constrained Linear Coding for Image Classification》的作者提供Matlab的代码实现，见http://www.ifp.illinois.edu/~jyang29/LLC.htm。

下面是根据作者的代码，基于OpenCV，实现的C++版的LLC：

Matlab Code：www.ifp.illinois.edu/~jyang29/LLC.htm

<span style="font-size:12px;">    cv::Mat BowModel::findKNN(cv::Mat &codebook, cv::Mat &input, int k) {  
      int nbase = codebook.rows;  
      int nquery = input.rows;  
      Mat ii = input.mul(input);  
      Mat cc = codebook.mul(codebook);  
        
      Mat sii(nquery,1,CV_32FC1);  
      sii.setTo(0);  
      Mat scc(nbase,1,CV_32FC1);  
      scc.setTo(0);  
      for (int i = 0; i<ii.rows; i++) {  
        for (int j = 0; j<ii.cols; j++) {  
            sii.at<float>(i,0) += ii.at<float>(i,j);  
        }  
      }  
      for (int i = 0; i<cc.rows; i++) {  
        for (int j = 0; j<cc.cols; j++) {  
            scc.at<float>(i,0) += cc.at<float>(i,j);  
        }  
      }  
        
      Mat D(nquery,nbase,CV_32FC1);  
      for (int i = 0; i<nquery; i++) {  
        for (int j = 0; j<nbase; j++) {  
            D.at<float>(i,j) = sii.at<float>(i,0);  
        }  
      }  
        
      Mat ct;  
      transpose(codebook, ct);  
      Mat D1 = 2*input*ct;  
        
      Mat scct;  
      transpose(scc, scct);  
      Mat D2(nquery, nbase, CV_32FC1);  
      for (int i = 0; i<nquery; i++) {  
        for (int j = 0; j<nbase; j++) {  
            D2.at<float>(i,j) = scct.at<float>(0,j);  
        }  
      }  
        
      D = D - D1 + D2;  
      Mat SD;  
      sortIdx(D, SD, CV_SORT_EVERY_ROW+CV_SORT_ASCENDING);  
      Mat IDX(nquery,k,CV_8UC1);  
      for (int i = 0; i<nquery; i++) {  
        for (int j = 0; j<k; j++) {  
            IDX.at<uchar>(i,j) = SD.row(i).col(j).at<uchar>(0,0);  
        }  
      }  
        
      ii.release();  
      cc.release();  
      sii.release();  
      scc.release();  
      D.release();  
      ct.release();  
      D1.release();  
      scct.release();  
      D2.release();  
      SD.release();  
      return IDX;  
    }  
      
    cv::Mat BowModel::llccode(cv::Mat &codebook, cv::Mat &input, cv::Mat IDX, int k)  
    {  
        int nquery = input.rows;  
        int nbase = codebook.rows;  
        int dim = codebook.cols;  
      
        Mat II = Mat::eye(k, k, CV_32FC1);  
        Mat Coeff(nquery,nbase,CV_32FC1);  
        Coeff.setTo(0);  
        Mat z;  
        Mat z1(k,dim,CV_32FC1);  
        Mat z2(k,dim,CV_32FC1);  
        Mat C;  
        Mat un(k,1,CV_32FC1);  
        un.setTo(1);  
        Mat temp;  
        Mat temp2;  
        Mat w;  
        Mat wt;  
      
        for (int n = 0; n<nquery; n++) {  
            for (int i = 0; i<k; i++) {  
                for (int j = 0; j<dim; j++) {  
                    z1.at<float>(i,j) = codebook.at<float>(IDX.at<uchar>(n,i),j);  
                    z2.at<float>(i,j) = input.at<float>(n,j);  
                }  
            }  
            z = z1 - z2;  
            transpose(z, temp);  
            C = z*temp;  
            C = C + II*(1e-4)*trace(C)[0];  
            invert(C,temp2);  
            w = temp2*un;  
            float sum_w=0;  
            for (int i = 0; i<k; i++) {  
                sum_w += w.at<float>(i,0);  
            }  
            w = w/sum_w;  
            transpose(w, wt);  
            for (int i = 0; i<k; i++) {  
                Coeff.at<float>(n,IDX.at<uchar>(n,i)) = wt.at<float>(0,i);  
            }  
        }  
      
        II.release();  
        z.release();  
        z1.release();  
        z2.release();  
        C.release();  
        un.release();  
        temp.release();  
        temp2.release();  
        w.release();  
        wt.release();  
      
        return Coeff;  
    }  </span>