enum
{KMEANS_RANDOM_CENTERS=0, // Chooses random centers for k-Means initializationKMEANS_PP_CENTERS=2,     // Uses k-Means++ algorithm for initializationKMEANS_USE_INITIAL_LABELS=1 // Uses the user-provided labels for K-Means initialization
};
//! clusters the input data using k-Means algorithm
CV_EXPORTS_W double kmeans( InputArray data, int K, CV_OUT InputOutputArray bestLabels,TermCriteria criteria, int attempts,int flags, OutputArray centers=noArray() );

double kmeans( const Mat& data, int K, Mat& best_labels,TermCriteria criteria, int attempts,int flags, Mat* _centers )
{const int SPP_TRIALS = 3;int N = data.rows > 1 ? data.rows : data.cols;int dims = (data.rows > 1 ? data.cols : 1)*data.channels();int type = data.depth();bool simd = checkHardwareSupport(CV_CPU_SSE);attempts = std::max(attempts, 1);CV_Assert( type == CV_32F && K > 0 );Mat _labels;if( flags & CV_KMEANS_USE_INITIAL_LABELS ){CV_Assert( (best_labels.cols == 1 || best_labels.rows == 1) &&best_labels.cols*best_labels.rows == N &&best_labels.type() == CV_32S &&best_labels.isContinuous());best_labels.copyTo(_labels);}else{if( !((best_labels.cols == 1 || best_labels.rows == 1) &&best_labels.cols*best_labels.rows == N &&best_labels.type() == CV_32S &&best_labels.isContinuous()))best_labels.create(N, 1, CV_32S);_labels.create(best_labels.size(), best_labels.type());}int* labels = _labels.ptr<int>();Mat centers(K, dims, type), old_centers(K, dims, type);vector<int> counters(K);vector<Vec2f> _box(dims);Vec2f* box = &_box[0];double best_compactness = DBL_MAX, compactness = 0;RNG& rng = theRNG();int a, iter, i, j, k;if( criteria.type & TermCriteria::EPS )criteria.epsilon = std::max(criteria.epsilon, 0.);elsecriteria.epsilon = FLT_EPSILON;criteria.epsilon *= criteria.epsilon;if( criteria.type & TermCriteria::COUNT )criteria.maxCount = std::min(std::max(criteria.maxCount, 2), 100);elsecriteria.maxCount = 100;if( K == 1 ){attempts = 1;criteria.maxCount = 2;}const float* sample = data.ptr<float>(0);for( j = 0; j < dims; j++ )box[j] = Vec2f(sample[j], sample[j]);for( i = 1; i < N; i++ ){sample = data.ptr<float>(i);for( j = 0; j < dims; j++ ){float v = sample[j];box[j][0] = std::min(box[j][0], v);box[j][1] = std::max(box[j][1], v);}}for( a = 0; a < attempts; a++ ){double max_center_shift = DBL_MAX;for( iter = 0; iter < criteria.maxCount && max_center_shift > criteria.epsilon; iter++ ){swap(centers, old_centers);if( iter == 0 && (a > 0 || !(flags & KMEANS_USE_INITIAL_LABELS)) ){if( flags & KMEANS_PP_CENTERS )generateCentersPP(data, centers, K, rng, SPP_TRIALS);else{for( k = 0; k < K; k++ )generateRandomCenter(_box, centers.ptr<float>(k), rng);}}else{if( iter == 0 && a == 0 && (flags & KMEANS_USE_INITIAL_LABELS) ){for( i = 0; i < N; i++ )CV_Assert( (unsigned)labels[i] < (unsigned)K );}// compute centerscenters = Scalar(0);for( k = 0; k < K; k++ )counters[k] = 0;for( i = 0; i < N; i++ ){sample = data.ptr<float>(i);k = labels[i];float* center = centers.ptr<float>(k);for( j = 0; j <= dims - 4; j += 4 ){float t0 = center[j] + sample[j];float t1 = center[j+1] + sample[j+1];center[j] = t0;center[j+1] = t1;t0 = center[j+2] + sample[j+2];t1 = center[j+3] + sample[j+3];center[j+2] = t0;center[j+3] = t1;}for( ; j < dims; j++ )center[j] += sample[j];counters[k]++;}if( iter > 0 )max_center_shift = 0;for( k = 0; k < K; k++ ){float* center = centers.ptr<float>(k);if( counters[k] != 0 ){float scale = 1.f/counters[k];for( j = 0; j < dims; j++ )center[j] *= scale;}elsegenerateRandomCenter(_box, center, rng);if( iter > 0 ){double dist = 0;const float* old_center = old_centers.ptr<float>(k);for( j = 0; j < dims; j++ ){double t = center[j] - old_center[j];dist += t*t;}max_center_shift = std::max(max_center_shift, dist);}}}// assign labelscompactness = 0;for( i = 0; i < N; i++ ){sample = data.ptr<float>(i);int k_best = 0;double min_dist = DBL_MAX;for( k = 0; k < K; k++ ){const float* center = centers.ptr<float>(k);double dist = distance(sample, center, dims, simd);if( min_dist > dist ){min_dist = dist;k_best = k;}}compactness += min_dist;labels[i] = k_best;}}if( compactness < best_compactness ){best_compactness = compactness;if( _centers )centers.copyTo(*_centers);_labels.copyTo(best_labels);}}return best_compactness;
}
}

调用方法：

    const int kMeansItCount = 10;  //迭代次数  const int kMeansType = cv::KMEANS_PP_CENTERS; //Use kmeans++ center initialization by Arthur and Vassilvitskii  cv::Mat bgdLabels, fgdLabels; //记录背景和前景的像素样本集中每个像素对应GMM的哪个高斯模型，论文中的kn     //kmeans中参数_bgdSamples为：每行一个样本  //kmeans的输出为bgdLabels，里面保存的是输入样本集中每一个样本对应的类标签（样本聚为componentsCount类后）  kmeans( _fgdSamples, GMM::componentsCount, fgdLabels,  cv::TermCriteria( CV_TERMCRIT_ITER, kMeansItCount, 0.0), 0, kMeansType );  

************作者：hjimce     联系qq：1393852684 更多资源请关注我的博客：http://blog.csdn.net/hjimce                  原创文章，版权所有，转载请保留这两行信息****************