K-近鄰（K-Nearest Neighbors, KNN）是一種很好理解的分類演算法，簡單說來就是從訓練樣本中找出K個與其最相近的樣本，然後看這K個樣本中哪個類別的樣本多，則待判定的值（或說抽樣）就屬於這個類別。

KNN演算法的步驟

• 計算已知類別資料集中每個點與當前點的距離；
• 選取與當前點距離最小的K個點；
• 統計前K個點中每個類別的樣本出現的頻率；
• 返回前K個點出現頻率最高的類別作為當前點的預測分類。

OpenCV中使用CvKNearest

OpenCV中實現CvKNearest類可以實現簡單的KNN訓練和預測。

int main()
{
	float labels[10] = {0,0,0,0,0,1,1,1,1,1};
	Mat labelsMat(10, 1, CV_32FC1, labels);
	cout<<labelsMat<<endl;
	float trainingData[10][2];
	srand(time(0)); 
	for(int i=0;i<5;i++){
		trainingData[i][0] = rand()%255+1;
		trainingData[i][1] = rand()%255+1;
		trainingData[i+5][0] = rand()%255+255;
		trainingData[i+5][1] = rand()%255+255;
	}
	Mat trainingDataMat(10, 2, CV_32FC1, trainingData);
	cout<<trainingDataMat<<endl;
	CvKNearest knn;
	knn.train(trainingDataMat,labelsMat,Mat(), false, 2 );
	// Data for visual representation
	int width = 512, height = 512;
	Mat image = Mat::zeros(height, width, CV_8UC3);
	Vec3b green(0,255,0), blue (255,0,0);

	for (int i = 0; i < image.rows; ++i){
		for (int j = 0; j < image.cols; ++j){
			const Mat sampleMat = (Mat_<float>(1,2) << i,j);
			Mat response;
			float result = knn.find_nearest(sampleMat,1);
			if (result !=0){
				image.at<Vec3b>(j, i)  = green;
			}
			else  
				image.at<Vec3b>(j, i)  = blue;
		}
	}

		// Show the training data
		for(int i=0;i<5;i++){
			circle(	image, Point(trainingData[i][0],  trainingData[i][1]), 
				5, Scalar(  0,   0,   0), -1, 8);
			circle(	image, Point(trainingData[i+5][0],  trainingData[i+5][1]), 
				5, Scalar(255, 255, 255), -1, 8);
		}
		imshow("KNN Simple Example", image); // show it to the user
		waitKey(10000);

}
 

使用的是之前BP神經網路中的例子，分類結果如下：
預測函式find_nearest()除了輸入sample引數外還有些其他的引數：

float CvKNearest::find_nearest(const Mat& samples, int k, Mat* results=0, 
const float** neighbors=0, Mat* neighborResponses=0, Mat* dist=0 )

即，samples為樣本數*特徵數的浮點矩陣；K為尋找最近點的個數；results與預測結果；neibhbors為k*樣本數的指標陣列（輸入為const，實在不知為何如此設計）；neighborResponse為樣本數*k的每個樣本K個近鄰的輸出值；dist為樣本數*k的每個樣本K個近鄰的距離。

另一個例子

OpenCV refman也提供了一個類似的示例，使用CvMat格式的輸入引數：

int main( int argc, char** argv )
{
	const int K = 10;
	int i, j, k, accuracy;
	float response;
	int train_sample_count = 100;
	CvRNG rng_state = cvRNG(-1);
	CvMat* trainData = cvCreateMat( train_sample_count, 2, CV_32FC1 );
	CvMat* trainClasses = cvCreateMat( train_sample_count, 1, CV_32FC1 );
	IplImage* img = cvCreateImage( cvSize( 500, 500 ), 8, 3 );
	float _sample[2];
	CvMat sample = cvMat( 1, 2, CV_32FC1, _sample );
	cvZero( img );
	CvMat trainData1, trainData2, trainClasses1, trainClasses2;
	// form the training samples
	cvGetRows( trainData, &trainData1, 0, train_sample_count/2 );
	cvRandArr( &rng_state, &trainData1, CV_RAND_NORMAL, cvScalar(200,200), cvScalar(50,50) );
	cvGetRows( trainData, &trainData2, train_sample_count/2, train_sample_count );
	cvRandArr( &rng_state, &trainData2, CV_RAND_NORMAL, cvScalar(300,300), cvScalar(50,50) );
	cvGetRows( trainClasses, &trainClasses1, 0, train_sample_count/2 );
	cvSet( &trainClasses1, cvScalar(1) );
	cvGetRows( trainClasses, &trainClasses2, train_sample_count/2, train_sample_count );
	cvSet( &trainClasses2, cvScalar(2) );
	// learn classifier
	CvKNearest knn( trainData, trainClasses, 0, false, K );
	CvMat* nearests = cvCreateMat( 1, K, CV_32FC1);
	for( i = 0; i < img->height; i++ )
	{
		for( j = 0; j < img->width; j++ )
		{
			sample.data.fl[0] = (float)j;
			sample.data.fl[1] = (float)i;
			// estimate the response and get the neighbors’ labels
			response = knn.find_nearest(&sample,K,0,0,nearests,0);
			// compute the number of neighbors representing the majority
			for( k = 0, accuracy = 0; k < K; k++ )
			{
				if( nearests->data.fl[k] == response)
					accuracy++;
			}
			// highlight the pixel depending on the accuracy (or confidence)
			cvSet2D( img, i, j, response == 1 ?
				(accuracy > 5 ? CV_RGB(180,0,0) : CV_RGB(180,120,0)) :
				(accuracy > 5 ? CV_RGB(0,180,0) : CV_RGB(120,120,0)) );
		}
	}
	// display the original training samples
	for( i = 0; i < train_sample_count/2; i++ )
	{
		CvPoint pt;
		pt.x = cvRound(trainData1.data.fl[i*2]);
		pt.y = cvRound(trainData1.data.fl[i*2+1]);
		cvCircle( img, pt, 2, CV_RGB(255,0,0), CV_FILLED );
		pt.x = cvRound(trainData2.data.fl[i*2]);
		pt.y = cvRound(trainData2.data.fl[i*2+1]);
		cvCircle( img, pt, 2, CV_RGB(0,255,0), CV_FILLED );
	}
	cvNamedWindow( "classifier result", 1 );
	cvShowImage( "classifier result", img );
	cvWaitKey(0);
	cvReleaseMat( &trainClasses );
	cvReleaseMat( &trainData );
	return 0;
}
 

分類結果：
KNN的思想很好理解，也非常容易實現，同時分類結果較高，對異常值不敏感。但計算複雜度較高，不適於大資料的分類問題。