bool StaticSaliency::computeBinaryMap( InputArray _saliencyMap, OutputArray _binaryMap ) { Mat saliencyMap = _saliencyMap.getMat(); Mat labels = Mat::zeros( saliencyMap.rows * saliencyMap.cols, 1, 1 ); Mat samples = Mat_<float>( saliencyMap.rows * saliencyMap.cols, 1 ); Mat centers; TermCriteria terminationCriteria; terminationCriteria.epsilon = 0.2; terminationCriteria.maxCount = 1000; terminationCriteria.type = TermCriteria::COUNT + TermCriteria::EPS; int elemCounter = 0; for ( int i = 0; i < saliencyMap.rows; i++ ) { for ( int j = 0; j < saliencyMap.cols; j++ ) { samples.at<float>( elemCounter, 0 ) = saliencyMap.at<float>( i, j ); elemCounter++; } } kmeans( samples, 5, labels, terminationCriteria, 5, KMEANS_RANDOM_CENTERS, centers ); Mat outputMat = Mat_<float>( saliencyMap.size() ); int intCounter = 0; for ( int x = 0; x < saliencyMap.rows; x++ ) { for ( int y = 0; y < saliencyMap.cols; y++ ) { outputMat.at<float>( x, y ) = centers.at<float>( labels.at<int>( intCounter, 0 ), 0 ); intCounter++; } } //Convert outputMat = outputMat * 255; outputMat.convertTo( outputMat, CV_8U ); // adaptative thresholding using Otsu's method, to make saliency map binary _binaryMap.createSameSize(outputMat, outputMat.type()); Mat BinaryMap = _binaryMap.getMat(); threshold( outputMat, BinaryMap, 0, 255, THRESH_BINARY | THRESH_OTSU ); return true; }