/*
* This file is part of the LIRE project: http://lire-project.net
* LIRE is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* LIRE is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with LIRE; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
* We kindly ask you to refer the any or one of the following publications in
* any publication mentioning or employing Lire:
*
* Lux Mathias, Savvas A. Chatzichristofis. Lire: Lucene Image Retrieval –
* An Extensible Java CBIR Library. In proceedings of the 16th ACM International
* Conference on Multimedia, pp. 1085-1088, Vancouver, Canada, 2008
* URL: http://doi.acm.org/10.1145/1459359.1459577
*
* Lux Mathias. Content Based Image Retrieval with LIRE. In proceedings of the
* 19th ACM International Conference on Multimedia, pp. 735-738, Scottsdale,
* Arizona, USA, 2011
* URL: http://dl.acm.org/citation.cfm?id=2072432
*
* Mathias Lux, Oge Marques. Visual Information Retrieval using Java and LIRE
* Morgan & Claypool, 2013
* URL: http://www.morganclaypool.com/doi/abs/10.2200/S00468ED1V01Y201301ICR025
*
* Copyright statement:
* --------------------
* (c) 2002-2013 by Mathias Lux (mathias@juggle.at)
* http://www.semanticmetadata.net/lire, http://www.lire-project.net
*/
package net.semanticmetadata.lire.classifiers;
import net.semanticmetadata.lire.utils.StatsUtils;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.Set;
/**
* Created by Mathias on 23/09/2008.
*
* @author Mathias Lux, mathias@juggle.at
* @author Nektarios Anagnostopoulos, nek.anag@gmail.com
* @author Lazaros Tsochatzidis, ltsochat@ee.duth.gr
*/
public class KMeans {
protected int countAllFeatures = 0, numClusters = 512;
protected int length;
protected ArrayList<double[]> features = new ArrayList<double[]>();
protected Cluster[] clusters = null;
// protected HashMap<double[], Integer> featureIndex = null;
public KMeans(int numClusters) {
this.numClusters = numClusters;
}
public void addFeature(double[] feature) {
if (!hasNaNs(feature)){
features.add(feature);
countAllFeatures++;
}
}
public void init() {
if (features.size() < numClusters *2) {
System.err.println("WARNING: Please note that the number of local features, in this case " + features.size() + ", is" +
"smaller than the recommended minimum number, which is two times the number of visual words, in your case 2*" + numClusters +
". Please adapt your data and either use images with more local features or more images for creating the visual vocabulary.");
}
if (features.size() < numClusters + 1) {
System.err.println("CRITICAL: The number of features is smaller than the number of clusters. This cannot work as there has to be at least one " +
"feature per cluster. Aborting process now.");
System.out.println("features: " + features.size());
System.out.println("clusters: " + numClusters);
System.exit(1);
}
// find first clusters:
clusters = new Cluster[numClusters];
Set<Integer> medians = selectInitialMedians(numClusters);
assert(medians.size() == numClusters); // this has to be the same ...
Iterator<Integer> mediansIterator = medians.iterator();
double[] descriptor;
for (int i = 0; i < clusters.length; i++) {
descriptor = features.get(mediansIterator.next());
clusters[i] = new Cluster(new double[descriptor.length]);
System.arraycopy(descriptor, 0, clusters[i].mean, 0, descriptor.length);
}
length = features.get(0).length;
}
protected Set<Integer> selectInitialMedians(int numClusters) {
return StatsUtils.drawSample(numClusters, features.size());
}
/**
* Do one step and return the overall stress (squared error). You should do this until
* the error is below a threshold or doesn't change a lot in between two subsequent steps.
*
* @return
*/
public double clusteringStep() {
for (Cluster cluster : clusters) {
cluster.reset();
}
reOrganizeFeatures();
recomputeMeans();
return overallStress();
}
protected boolean hasNaNs(double[] histogram) {
boolean hasNaNs = false;
for (double next : histogram) {
if (Double.isNaN(next)) {
hasNaNs = true;
break;
}
}
if (hasNaNs) {
System.err.println("Found a NaN in init");
// System.out.println("image.identifier = " + image.identifier);
for (double v : histogram) {
System.out.print(v + ", ");
}
System.out.println("");
}
return hasNaNs;
}
/**
* Re-shuffle all features.
*/
protected void reOrganizeFeatures() {
double[] f;
Cluster best;
double v, minDistance;
for (int k = 0; k < features.size(); k++) {
f = features.get(k);
best = clusters[0];
minDistance = clusters[0].getDistance(f);
for (int i = 1; i < clusters.length; i++) {
v = clusters[i].getDistance(f);
if (minDistance > v) {
best = clusters[i];
minDistance = v;
}
}
best.assignMember(f);
}
}
/**
* Computes the mean per cluster (averaged vector)
*/
protected void recomputeMeans() {
int length = features.get(0).length;
Cluster cluster;
double[] mean;
for (int i = 0; i < clusters.length; i++) {
cluster = clusters[i];
if (cluster.getSize() == 1) {
System.err.println("** There is just one member in cluster " + i);
} else if (cluster.getSize() < 1) {
System.err.println("** There is NO member in cluster " + i);
// fill it with a random member?!?
int index = (int) Math.floor(Math.random()*features.size());
clusters[i].assignMember(features.get(index));
}
cluster.move();
}
}
/**
* Squared error in classification.
*
* @return
*/
protected double overallStress() {
double v = 0;
for (Cluster cluster : clusters) {
v+=cluster.getStress();
}
return v;
}
/**
* Get the number of desired clusters.
*
* @return
*/
public int getNumClusters() {
return numClusters;
}
public int getFeatureCount() {
return countAllFeatures;
}
public Cluster[] getClusters() {
return clusters;
}
// public void setNumClusters(int numClusters) {
// this.numClusters = numClusters;
// }
//
// private HashMap<double[], Integer> createIndex() {
// featureIndex1 = new HashMap<double[], Integer>(features1.size());
// for (int i = 0; i < clusters1.length; i++) {
// Cluster cluster = clusters1[i];
// for (Iterator<Integer> fidit = cluster.members.iterator(); fidit.hasNext(); ) {
// int fid = fidit.next();
// featureIndex1.put(features1.get(fid), i);
// }
// }
// return featureIndex1;
// }
//
// /**
// * Used to find the cluster of a feature actually used in the clustering process (so
// * it is known by the k-means class).
// *
// * @param f the feature to search for
// * @return the index of the Cluster
// */
// public int getClusterOfFeature(FeatureVector f) {
// if (featureIndex1 == null) createIndex();
// return featureIndex1.get(f);
// }
}