/*
* 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: 11.07.13 10:33
*/
package net.semanticmetadata.lire.imageanalysis.features.global;
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 java.awt.image.BufferedImage;
import java.awt.image.WritableRaster;
/**
* Simple fuzzy 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). Also there is the
* rank of the pixel joint.
*
* @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 FuzzyOpponentHistogram implements GlobalFeature {
final double sq2 = Math.sqrt(2d);
final double sq6 = Math.sqrt(3d);
final double sq3 = Math.sqrt(6d);
double o1, o2, o3;
double[] o1f = new double[4];
double[] o2f = new double[4];
double[] o3f = new double[4];
private int[] tmpIntensity = new int[1];
double[] descriptor;
public void extract(BufferedImage bimg) {
// extract:
double[][] histogram = new double[64][9];
for (int i = 0; i < histogram.length; i++) {
for (int j = 0; j < histogram[i].length; j++)
histogram[i][j] = 0;
}
WritableRaster grey = ImageUtils.getGrayscaleImage(bimg).getRaster();
WritableRaster raster = bimg.getRaster();
int[] px = new int[3];
int[] intens = new int[1];
int colorPos;
for (int x = 1; x < raster.getWidth() - 1; x++) {
for (int y = 1; y < raster.getHeight() - 1; y++) {
raster.getPixel(x, y, px);
o1 = (double) (px[0] - px[1]) / sq2;
o2 = (double) (px[0] + px[1] - 2 * px[2]) / sq6;
o3 = (double) (px[0] + px[1] + px[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.
getFuzzyMembership(o1, o1f);
getFuzzyMembership(o2, o2f);
getFuzzyMembership(o3, o3f);
int rank = 0;
grey.getPixel(x, y, intens);
if (getIntensity(x - 1, y - 1, grey) > intens[0]) rank++;
if (getIntensity(x, y - 1, grey) > intens[0]) rank++;
if (getIntensity(x + 1, y - 1, grey) > intens[0]) rank++;
if (getIntensity(x - 1, y + 1, grey) > intens[0]) rank++;
if (getIntensity(x, y + 1, grey) > intens[0]) rank++;
if (getIntensity(x + 1, y + 1, grey) > intens[0]) rank++;
if (getIntensity(x - 1, y, grey) > intens[0]) rank++;
if (getIntensity(x + 1, y, grey) > intens[0]) rank++;
for (int i = 0; i < o1f.length; i++) {
if (o1f[i] == 0) continue;
for (int j = 0; j < o2f.length; j++) {
if (o2f[j] == 0) continue;
for (int k = 0; k < o3f.length; k++) {
if (o3f[k] == 0) continue;
colorPos = i + j * 3 + k * 3 * 3;
histogram[colorPos][rank]+=o1f[i]*o2f[j]*o3f[k];
}
}
}
}
}
// normalize with max norm & quantize to [0,127]:
descriptor = new double[64*9];
double max = 0;
for (int i = 0; i < histogram.length; i++) {
for (int j = 0; j < histogram[i].length; j++)
max = Math.max(histogram[i][j], max);
}
for (int i = 0; i < histogram.length; i++) {
for (int j = 0; j < histogram[i].length; j++)
descriptor[i+27*j] = Math.floor(127d * (histogram[i][j] / max));
}
}
/**
* Creates a membership variable for each of the three bins given in out[]
*
* @param in
* @param out the array to put the membership values in.
*/
private void getFuzzyMembership(double in, double[] out) {
out[0] = 0d;
out[1] = 0d;
out[2] = 0d;
out[3] = 0d;
if (in <= 0.15) {
out[0] = 1d;
} else if (in > 0.15 && in < 0.25) {
out[0] = ((in - 0.15) * 10.0);
out[1] = 1d - out[0];
} else if (in >= 0.25 && in <= 0.45) {
out[1] = 1d;
} else if (in > 0.45 && in < 0.55) {
out[1] = ((in - 0.45) * 10.0);
out[2] = 1d - out[1];
} else if (in >= 0.55 && in <= 0.75) {
out[2] = 1d;
} else if (in > 0.75 && in < 0.85) {
out[2] = ((in - 0.75) * 10.0);
out[3] = 1d - out[2];
} else if (in >= 0.85) {
out[3] = 1d;
}
}
private int getIntensity(int x, int y, WritableRaster grey) {
grey.getPixel(x, y, tmpIntensity);
return tmpIntensity[0];
}
public byte[] getByteArrayRepresentation() {
byte[] result = new byte[descriptor.length];
for (int i = 0; i < result.length; i++) {
result[i] = (byte) descriptor[i];
}
return result;
}
public void setByteArrayRepresentation(byte[] in) {
descriptor = new double[in.length];
for (int i = 0; i < descriptor.length; i++) {
descriptor[i] = in[i];
}
}
public void setByteArrayRepresentation(byte[] in, int offset, int length) {
descriptor = new double[length];
for (int i = offset; i < length; i++) {
descriptor[i] = in[i];
}
}
public double[] getFeatureVector() {
return descriptor;
}
@Override
public double getDistance(LireFeature feature) {
if (!(feature instanceof FuzzyOpponentHistogram))
throw new UnsupportedOperationException("Wrong descriptor.");
return MetricsUtils.jsd(((FuzzyOpponentHistogram) feature).descriptor, descriptor);
}
// public String getStringRepresentation() {
// StringBuilder sb = new StringBuilder(descriptor.length * 2 + 25);
// sb.append("ophist");
// sb.append(' ');
// sb.append(descriptor.length);
// sb.append(' ');
// for (double aData : descriptor) {
// 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.");
// descriptor = new double[Integer.parseInt(st.nextToken())];
// for (int i = 0; i < descriptor.length; i++) {
// if (!st.hasMoreTokens())
// throw new IndexOutOfBoundsException("Too few numbers in string representation.");
// descriptor[i] = Integer.parseInt(st.nextToken());
// }
//
// }
@Override
public String getFeatureName() {
return "Fuzzy Opponent Histogram";
}
@Override
public String getFieldName() {
return "f_fuzopphis";
}
}