K Nearest Neighbors in CSharp

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UNDER COSTRUCTION, PLEASE SEE THIS POST IN RESERVE COPY

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CLICK HERE

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This example requires Emgu CV 1.5.0.0

What is a K Nearest Neighbors Classifier

According to wikipedia,

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification. It can also be used for regression.

Source Code

<source lang="csharp"> using System.Drawing; using Emgu.CV.Structure; using Emgu.CV.ML; using Emgu.CV.ML.Structure;

...

int K = 10; int trainSampleCount = 100;

1. region Generate the traning data and classes

Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2); Matrix<float> trainClasses = new Matrix<float>(trainSampleCount, 1);

Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);

Matrix<float> sample = new Matrix<float>(1, 2);

Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1); trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50)); Matrix<float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1); trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));

Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1); trainClasses1.SetValue(1); Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1); trainClasses2.SetValue(2);

1. endregion

Matrix<float> results, neighborResponses; results = new Matrix<float>(sample.Rows, 1); neighborResponses = new Matrix<float>(sample.Rows, K); //dist = new Matrix<float>(sample.Rows, K);

using (KNearest knn = new KNearest(trainData, trainClasses, null, false, K)) {

  for (int i = 0; i < img.Height; i++)
  {
     for (int j = 0; j < img.Width; j++)
     {
        sample.Data[0, 0] = j;
        sample.Data[0, 1] = i;

        //Matrix<float> nearestNeighbors = new Matrix<float>(K* sample.Rows, sample.Cols);
        // estimates the response and get the neighbors' labels
        float response = knn.FindNearest(sample, K, results, null, neighborResponses, null);

        int accuracy = 0;
        // compute the number of neighbors representing the majority
        for (int k = 0; k < K; k++)
        {
           if (neighborResponses.Data[0, k] == response)
              accuracy++;
        }
        // highlight the pixel depending on the accuracy (or confidence)
        img[i, j] =
        response == 1 ?
            (accuracy > 5 ? new Bgr(90, 0, 0) : new Bgr(90, 60, 0)) :
            (accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(60, 90, 0));
     }
  }

}

// display the original training samples for (int i = 0; i < (trainSampleCount >> 1); i++) {

  PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
  img.Draw(new CircleF(p1, 2.0f), new Bgr(255, 100, 100), -1);
  PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
  img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 255, 100), -1);

}

Emgu.CV.UI.ImageViewer.Show(img); </source>

Result