It's also available through the download page.

Please note that the code itself is at least 4 years old and there are far better ways to solve some things. I therefore suggest that it is used for studying purposes only. Comments and improvments are very much appreciated.

My blogpost on K-means clustering has the highest number of views, so people are probably interested in it. Sadly enough I lost the source code of the K-means action a while ago. Last week I needed an external harddisk to make a back-up of some files. There was already some content on the disk. I found quite some pieces of code including the K-means code. Although it is quite simple code operating on (if I remember correctly 8-bit) greyscale images, it might give some insights in how to do this.

The whole code file is presented below. For more information you can view my earlier blogpost on K-means clustering.

package actions;

import java.awt.Color;
import java.awt.image.BufferedImage;
import java.util.ArrayList;

/**
 * This KMeansAction performs a K-means clustering action on a BufferedImage
 * @author Patrick van Kouteren
 *
 */

public class KMeansAction {

		BufferedImage image_temp;
		boolean not_terminated;
		int loops, changedPixels;
		int[] histogram;
		ArrayList classes;
		int [] lowerbounds;
		public final static int MEAN_BY_MOD = 1;
		public final static int MEAN_BY_SPACE = 2;
		public final static int MEAN_AT_RANDOM = 3;

		/**
		 * Controls the actual work:
		 * - Initialization
		 * - Loop until termination condition is met
		 *  + for each pixel: assign pixel to a class such that the distance from the pixel to the mean of that class is minimized
		 *  + for each class: recalculate the means of the class based on pixels belonging to that class
		 * - End loop
		 * @param image
		 * @param bins (k)
		 * @param histogram
		 */
		public KMeansAction(BufferedImage image, int bins, int[]histogram, int initway) {
			this.histogram = histogram;
			lowerbounds = new int[bins];
			initialize(image, bins, initway);
			calculateBounds();
			while (not_terminated) {
				recalculateMeans();
				loops++;
				checkTermination();
				}
			processImage(image, bins);
		}

		/**
		 * Set the new color values for the image
		 * @param image
		 */
		private void processImage(BufferedImage image, int bins) {
			int delta = 255 / (bins-1);
			for (int h = 0; h < image.getHeight(); h++){
				for (int w = 0; w < image.getWidth(); w++){
					Color rgb = new Color(image.getRGB(w, h));
					int grey = rgb.getRed();
					for (int i = 0; i classes.get(i).lowerbound && grey < classes.get(i).upperbound) {
							int g = i*delta;
							image_temp.setRGB(w,h,(new Color(g, g, g)).getRGB());
						}
					}
				}
			}
		}

		/**
		 * Returns the image created by the processImage method
		 * @return the result image
		 */
		public BufferedImage getResultImage() {
			return image_temp;
		}

		/**
		 * Just for fun: returns the number of loops which were needed for getting a stable result
		 * @return number of loops for stable result
		 */
		public int getLoops(){
			return loops;
		}

		/**
		 * Initializes the algorithm. Creates k ClusterClasses and puts them into a LinkedList
		 * @param image
		 * @param bins
		 */
		@SuppressWarnings("unchecked")
		private void initialize(BufferedImage image, int bins, int initway){
			image_temp = image;
			loops = 0;
			changedPixels = 0;
			not_terminated = true;
			classes = new ArrayList();
			for (int i = 0; i < bins; i++) {
				ClusterClass cc = new ClusterClass(createMean(initway, bins, i, image));
				classes.add(cc);
			}

		}

		/**
		 * Controls the calculations of the upper- and lowerbounds of ClusterClasses and sets them
		 *
		 */
		private void calculateBounds() {
			for (int i = 0; i < classes.size(); i++){
				int lb = calculateLowerBound(classes.get(i));
				lowerbounds[i] = lb;
				classes.get(i).setBounds(lb,calculateUpperBound(classes.get(i)) );
				}
		}

		/**
		 * Does the actual calculation of the lowerbound
		 * @param ClusterClass
		 * @return Lowerbound
		 */
		private int calculateLowerBound(ClusterClass cc) {
			int cMean = cc.getMean();
			int currentBound = 0;
			for (int i = 0; i< classes.size(); i++) { 					if (cMean > classes.get(i).getMean()) {
						currentBound = Math.max((cMean + classes.get(i).getMean())/2, currentBound);
					}
					else {
					}
				}
			return currentBound;
			}

		/**
		 * Does the actual calculation of the upperbound
		 * @param ClusterClass
		 * @return Upperbound
		 */
		private int calculateUpperBound(ClusterClass cc) {
				int cMean = cc.getMean();
				int currentBound = 255;
				for (int i = 0; i< classes.size(); i++) {
						if (cMean < classes.get(i).getMean()) {
							currentBound = Math.min((cMean + classes.get(i).getMean())/2, currentBound);
						}
						else {}
					}
				return currentBound;
				}

		/**
		 * Takes care of the recalculation of the means of the ClusterClasses
		 *
		 */
		private void recalculateMeans() {
			for (int i = 0; i= 50) {
				not_terminated = false;
			}
			if (changedPixels <= 300) {
				not_terminated = false;
			}
		}

		private void calculateChangedPixels() {
			int changed = 0;
			for (int i = 0; i< lowerbounds[i]) {
					for (int j = c; j lowerbounds[i]) {
					for (int j = lowerbounds[i]; j< image.getHeight(); h++){
					for (int w = 0; w < image.getWidth(); w++){
						pixelindex+=1;
						if (pixelindex % bins == index) {
							Color rgb = new Color(image.getRGB(w, h));
							sum+= rgb.getRed();
							value+=1;
						}
					}}
				return sum/value;

			case MEAN_BY_SPACE:
				return (int)(255 / (bins-1) * index);
			case MEAN_AT_RANDOM:
				Double dmean = Math.random() * 255;
				return (int) Math.floor(dmean);
			default:
				return 0;
			}
		}
}</pre>
In addition to this, the custom class ClusterClass is defined as:
<pre lang="java">package actions;

/**
 * The ClusterClass is just a class holding the important cluster properties.
 * @author Patrick van Kouteren
 *
 */

public class ClusterClass {
	int mean, upperbound, lowerbound;

	public ClusterClass(int m) {
		mean = m;
	}

	public void setBounds(int lb, int ub) {
		lowerbound = lb;
		upperbound = ub;
	}

	public void setMean(int i) {
		mean = i;
	}

	public int getMean() {
		return mean;
	}

	public int getLowerBound() {
		return lowerbound;
	}

	public int getUpperBound() {
		return upperbound;
	}

	public void calculateMean(int [] histogram) {
		int tempMean = 0;
		int counter = 0;
		for (int i = lowerbound; i&lt;= upperbound; i++) {
			counter += histogram[i];
			tempMean += histogram[i] * i;
		}
		mean = tempMean / counter;
	}

}

The source code might not be completely visible. It can be viewed in a blank screen here. As mentioned in the replies to this post, I forgot to add the ClusterClass. It can be viewed in a blank screen here.