/*
* 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
*
* Updated: 07.08.13 12:18
*/
package net.semanticmetadata.lire.imageanalysis.features.global;
import net.semanticmetadata.lire.builders.DocumentBuilder;
import net.semanticmetadata.lire.imageanalysis.features.GlobalFeature;
import net.semanticmetadata.lire.imageanalysis.features.LireFeature;
import net.semanticmetadata.lire.utils.ImageUtils;
import net.semanticmetadata.lire.utils.MetricsUtils;
import net.semanticmetadata.lire.utils.SerializationUtils;
import java.awt.image.BufferedImage;
import java.awt.image.WritableRaster;
/**
* Simple 64 bin Opponent Histogram, based on the Opponent color space as described in van de Sande, Gevers & Snoek (2010)
* "Evaluating Color Descriptors for Object and Scene Recognition", IEEE PAMI (see BibTeX in the source code).
*
*
* @author Mathias Lux, mathias@juggle.at
* Date: 18.12.12
* Time: 11:53
*/
/*
@ARTICLE{Sande2010,
author={van de Sande, K.E.A. and Gevers, T. and Snoek, C.G.M.},
journal={Pattern Analysis and Machine Intelligence, IEEE Transactions on},
title={Evaluating Color Descriptors for Object and Scene Recognition},
year={2010},
month={sept. },
volume={32},
number={9},
pages={1582 -1596},
doi={10.1109/TPAMI.2009.154},
ISSN={0162-8828},
}
*/
public class OpponentHistogram implements GlobalFeature {
final double sq2 = Math.sqrt(2d);
final double sq6 = Math.sqrt(3d);
final double sq3 = Math.sqrt(6d);
double[] descriptor;
double o1, o2, o3;
double tmpVal, tmpSum;
byte[] histogram = new byte[64];
@Override
public void extract(BufferedImage bimg) {
// check if it's (i) RGB and (ii) 8 bits per pixel.
bimg = ImageUtils.get8BitRGBImage(bimg);
// extract:
double[] histogram = new double[64];
for (int i = 0; i < histogram.length; i++) {
histogram[i] = 0;
}
WritableRaster raster = bimg.getRaster();
int[] px = new int[3*(raster.getHeight()-2)];
int colorPos;
for (int x = 1; x < raster.getWidth() - 1; x++) {
raster.getPixels(x, 1, 1, raster.getHeight()-2, px);
for (int y = 0; y < raster.getHeight() - 2; y++) {
o1 = (double) (px[y*3] - px[y*3+1]) / sq2;
o2 = (double) (px[y*3] + px[y*3+1] - 2 * px[y*3+2]) / sq6;
o3 = (double) (px[y*3] + px[y*3+1] + px[y*3+2]) / sq3;
// Normalize ... easier to handle.
o1 = (o1 + 255d / sq2) / (510d / sq2);
o2 = (o2 + 510d / sq6) / (1020d / sq6);
o3 = o3 / (3d * 255d / sq3);
// get the array position.
colorPos = (int) Math.min(Math.floor(o1 * 4d), 3d) + (int) Math.min(Math.floor(o2 * 4d), 3d) * 4 + (int) Math.min(3d, Math.floor(o3 * 4d)) * 4 * 4;
histogram[colorPos]++;
}
}
// normalize with max norm & quantize to [0,127]:
double max = 0;
for (int i = 0; i < histogram.length; i++) {
max = Math.max(histogram[i], max);
}
for (int i = 0; i < histogram.length; i++) {
this.histogram[i] = (byte) Math.floor(127d * (histogram[i] / max));
}
}
@Override
public byte[] getByteArrayRepresentation() {
byte[] result = new byte[histogram.length];
for (int i = 0; i < result.length; i++) {
result[i] = histogram[i];
}
return result;
}
@Override
public void setByteArrayRepresentation(byte[] in) {
for (int i = 0; i < histogram.length; i++) {
histogram[i] = in[i];
}
}
@Override
public void setByteArrayRepresentation(byte[] in, int offset, int length) {
for (int i = 0; i < length; i++) {
histogram[i] = in[i+offset];
}
}
@Override
public double[] getFeatureVector() {
return SerializationUtils.castToDoubleArray(histogram);
}
@Override
public double getDistance(LireFeature feature) {
if (!(feature instanceof OpponentHistogram))
throw new UnsupportedOperationException("Wrong descriptor.");
return MetricsUtils.jsd(((OpponentHistogram) feature).histogram, histogram);
}
public double getDistance(byte[] h1, byte[] h2) {
return getDistance(h1, 0, h1.length, h2, 0, h2.length);
}
/**
* Jeffrey Divergence or Jensen-Shannon divergence (JSD) from
* Deselaers, T.; Keysers, D. & Ney, H. Features for image retrieval:
* an experimental comparison Inf. Retr., Kluwer Academic Publishers, 2008, 11, 77-107
* @param h1
* @param offset1
* @param length1
* @param h2
* @param offset2
* @param length2
* @return
*/
public double getDistance(byte[] h1, int offset1, int length1, byte[] h2, int offset2, int length2) {
// double sum = 0f;
// for (int i = 0; i < h1.length; i++) {
// sum += (h1[i] > 0 ? (h1[i] / 2f) * Math.log((2f * h1[i]) / (h1[i] + h2[i])) : 0) +
// (h2[i] > 0 ? (h2[i] / 2f) * Math.log((2f * h2[i]) / (h1[i] + h2[i])) : 0);
// }
// return (float) sum;
tmpSum = 0;
for (int i = 0; i < length1; i++) {
tmpVal = (double) (h1[i+offset1] + h2[i+offset2]);
tmpSum += (h1[i+offset1] > 0 ? ((double) h1[i+offset1] / 2d) * Math.log((2d * h1[i+offset1]) / tmpVal) : 0) +
(h2[i+offset2] > 0 ? ((double) h2[i+offset2] / 2d) * Math.log((2d * h2[i+offset2]) / tmpVal) : 0);
}
return tmpSum;
}
// public String getStringRepresentation() {
// StringBuilder sb = new StringBuilder(histogram.length * 2 + 25);
// sb.append("ophist");
// sb.append(' ');
// sb.append(histogram.length);
// sb.append(' ');
// for (double aData : histogram) {
// sb.append((int) aData);
// sb.append(' ');
// }
// return sb.toString().trim();
// }
//
// public void setStringRepresentation(String s) {
// StringTokenizer st = new StringTokenizer(s);
// if (!st.nextToken().equals("ophist"))
// throw new UnsupportedOperationException("This is not a OpponentHistogram descriptor.");
// for (int i = 0; i < histogram.length; i++) {
// if (!st.hasMoreTokens())
// throw new IndexOutOfBoundsException("Too few numbers in string representation.");
// histogram[i] = (byte) Integer.parseInt(st.nextToken());
// }
//
// }
@Override
public String getFeatureName() {
return "OpponentHistogram";
}
@Override
public String getFieldName() {
return DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM;
}
}