private void kmeans() { // Retrieve the number of clusters int k = (int)numClusters.Value; // Load original image Bitmap image = Properties.Resources.leaf; // Create conversors ImageToArray imageToArray = new ImageToArray(min: -1, max: +1); ArrayToImage arrayToImage = new ArrayToImage(image.Width, image.Height, min: -1, max: +1); // Transform the image into an array of pixel values double[][] pixels; imageToArray.Convert(image, out pixels); // Create a K-Means algorithm using given k and a // square euclidean distance as distance metric. KMeans kmeans = new KMeans(k, Distance.SquareEuclidean); // Compute the K-Means algorithm until the difference in // cluster centroids between two iterations is below 0.05 int[] idx = kmeans.Compute(pixels, 0.05); // Replace every pixel with its corresponding centroid pixels.ApplyInPlace((x, i) => kmeans.Clusters.Centroids[idx[i]]); // Show resulting image in the picture box Bitmap result; arrayToImage.Convert(pixels, out result); pictureBox.Image = result; }