/*
* 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 10:31
*/
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 java.awt.image.BufferedImage;
import java.awt.image.WritableRaster;
import java.util.Arrays;
/**
* A simple implementation of the original local binary pattern texture feature.
* @author Mathias Lux, mathias@juggle.at
* Time: 21.06.13 13:51
*/
public class LocalBinaryPatterns implements GlobalFeature {
double[] histogram = new double[256];
@Override
public void extract(BufferedImage image) {
Arrays.fill(histogram, 0d);
extractRadiusWithOne(image);
}
private void extractRadiusWithOne(BufferedImage image) {
// first convert to intensity only.
WritableRaster raster = ImageUtils.getGrayscaleImage(image).getRaster();
// cached pixel array
int[] pixel = new int[9];
int bin = 0;
// now fill histogram according to LBP definition.
for (int x = 0; x < raster.getWidth() - 2; x++) {
for (int y = 0; y < raster.getHeight() - 2; y++) {
raster.getPixels(x, y, 3, 3, pixel);
if (pixel[0] >= pixel[4]) bin += 1;
if (pixel[1] >= pixel[4]) bin += 2;
if (pixel[2] >= pixel[4]) bin += 4;
if (pixel[5] >= pixel[4]) bin += 8;
if (pixel[8] >= pixel[4]) bin += 16;
if (pixel[7] >= pixel[4]) bin += 32;
if (pixel[6] >= pixel[4]) bin += 64;
if (pixel[3] >= pixel[4]) bin += 128;
histogram[bin]++;
bin = 0;
}
}
// normalize & quantize histogram.
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++) {
histogram[i] = Math.floor((histogram[i] / max) * 127);
}
}
@SuppressWarnings("unused")
private void extractWithRadiusTwo(BufferedImage image) {
// first convert to intensity only.
WritableRaster raster = ImageUtils.getGrayscaleImage(image).getRaster();
// cached pixel array
int[] pixel = new int[25];
int bin = 0;
// now fill histogram according to LBP definition.
for (int x = 0; x < raster.getWidth() - 4; x++) {
for (int y = 0; y < raster.getHeight() - 4; y++) {
raster.getPixels(x, y, 5, 5, pixel);
if (pixel[1] >= pixel[12]) bin += 1;
if (pixel[2] >= pixel[12]) bin += 2;
if (pixel[3] >= pixel[12]) bin += 4;
if (pixel[9] >= pixel[12]) bin += 8;
if (pixel[14] >= pixel[12]) bin += 16;
if (pixel[19] >= pixel[12]) bin += 32;
if (pixel[23] >= pixel[12]) bin += 64;
if (pixel[22] >= pixel[12]) bin += 128;
if (pixel[21] >= pixel[12]) bin += 256;
if (pixel[15] >= pixel[12]) bin += 512;
if (pixel[10] >= pixel[12]) bin += 1024;
if (pixel[5] >= pixel[12]) bin += 2048;
histogram[bin]++;
bin = 0;
}
}
// normalize & quantize histogram.
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++) {
histogram[i] = Math.floor((histogram[i] / max) * 128);
}
}
@Override
public byte[] getByteArrayRepresentation() {
byte[] rep = new byte[histogram.length];
for (int i = 0; i < histogram.length; i++) {
rep[i] = (byte) histogram[i];
}
return rep;
}
@Override
public void setByteArrayRepresentation(byte[] in) {
setByteArrayRepresentation(in, 0, in.length);
}
@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 histogram;
}
@Override
public double getDistance(LireFeature feature) {
return MetricsUtils.distL1(histogram, feature.getFeatureVector());
}
// @Override
// public String getStringRepresentation() {
// throw new UnsupportedOperationException("Not implemented!");
// }
//
// @Override
// public void setStringRepresentation(String s) {
// throw new UnsupportedOperationException("Not implemented!");
// }
@Override
public String getFeatureName() {
return "Local Binary Patterns";
}
@Override
public String getFieldName() {
return DocumentBuilder.FIELD_NAME_LOCAL_BINARY_PATTERNS;
}
}