/*
* 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: 23.06.13 18:16
*/
package net.semanticmetadata.lire.searchers;
import net.semanticmetadata.lire.builders.DocumentBuilder;
import net.semanticmetadata.lire.imageanalysis.features.GlobalFeature;
import net.semanticmetadata.lire.imageanalysis.features.global.OpponentHistogram;
import net.semanticmetadata.lire.utils.ImageUtils;
import org.apache.lucene.document.Document;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.MultiFields;
import org.apache.lucene.util.Bits;
import java.awt.image.BufferedImage;
import java.io.IOException;
import java.util.TreeSet;
import java.util.logging.Logger;
/**
* This file is part of the Caliph and Emir project: http://www.SemanticMetadata.net
* <br>Date: 01.02.2006
* <br>Time: 00:17:02
*
* @author Mathias Lux, mathias@juggle.at
*/
public class FastOpponentImageSearcher extends AbstractImageSearcher {
protected Logger logger = Logger.getLogger(getClass().getName());
private OpponentHistogram cachedInstance = null;
private int maxHits = 10;
protected TreeSet<SimpleResult> docs;
private byte[] tempBinaryValue;
private double maxDistance;
private float overallMaxDistance;
public FastOpponentImageSearcher(int maxHits) {
this.maxHits = maxHits;
docs = new TreeSet<SimpleResult>();
this.cachedInstance = new OpponentHistogram();
}
public ImageSearchHits search(BufferedImage image, IndexReader reader) throws IOException {
logger.finer("Starting extraction.");
OpponentHistogram globalFeature = null;
SimpleImageSearchHits searchHits = null;
globalFeature = new OpponentHistogram();
// Scaling image is especially with the correlogram features very important!
BufferedImage bimg = image;
if (Math.max(image.getHeight(), image.getWidth()) > DocumentBuilder.MAX_IMAGE_DIMENSION) {
bimg = ImageUtils.scaleImage(image, DocumentBuilder.MAX_IMAGE_DIMENSION);
}
globalFeature.extract(bimg);
logger.fine("Extraction from image finished");
double maxDistance = findSimilar(reader, globalFeature);
searchHits = new SimpleImageSearchHits(this.docs, (float) maxDistance);
return searchHits;
}
/**
* @param reader
* @param globalFeature
* @return the maximum distance found for normalizing.
* @throws java.io.IOException
*/
protected double findSimilar(IndexReader reader, GlobalFeature globalFeature) throws IOException {
maxDistance = -1f;
// clear result set ...
docs.clear();
// Needed for check whether the document is deleted.
Bits liveDocs = MultiFields.getLiveDocs(reader);
Document d;
double tmpDistance;
int docs = reader.numDocs();
byte[] histogram = globalFeature.getByteArrayRepresentation();
for (int i = 0; i < docs; i++) {
if (reader.hasDeletions() && !liveDocs.get(i)) continue; // if it is deleted, just ignore it.
d = reader.document(i);
tmpDistance = getDistance(d, histogram);
assert (tmpDistance >= 0);
// calculate the overall max distance to normalize score afterwards
// if (overallMaxDistance < tmpDistance) {
// overallMaxDistance = tmpDistance;
// }
// if it is the first document:
if (maxDistance < 0) {
maxDistance = tmpDistance;
}
// if the array is not full yet:
if (this.docs.size() < maxHits) {
this.docs.add(new SimpleResult( tmpDistance, i));
if (tmpDistance > maxDistance) maxDistance = tmpDistance;
} else if (tmpDistance < maxDistance) {
// if it is nearer to the sample than at least on of the current set:
// remove the last one ...
this.docs.remove(this.docs.last());
// add the new one ...
this.docs.add(new SimpleResult(tmpDistance, i));
// and set our new distance border ...
maxDistance = this.docs.last().getDistance();
}
}
return maxDistance;
}
/**
* Main similarity method called for each and every document in the index.
*
* @param document
* @param histogram
* @return the distance between the given feature and the feature stored in the document.
*/
protected double getDistance(Document document, byte[] histogram) {
if (document.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue() != null && document.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().length > 0) {
return cachedInstance.getDistance(histogram, 0, histogram.length,
document.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().bytes,
document.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().offset,
document.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().length);
} else {
logger.warning("No feature stored in this document!");
}
return 0d;
}
public ImageSearchHits search(Document doc, IndexReader reader) throws IOException {
SimpleImageSearchHits searchHits = null;
OpponentHistogram globalFeature = new OpponentHistogram();
if (doc.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue() != null && doc.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().length > 0)
globalFeature.setByteArrayRepresentation(doc.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().bytes,
doc.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().offset,
doc.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().length);
double maxDistance = findSimilar(reader, globalFeature);
searchHits = new SimpleImageSearchHits(this.docs, (float) maxDistance);
return searchHits;
}
public ImageDuplicates findDuplicates(IndexReader reader) throws IOException {
throw new UnsupportedOperationException("not implemented");
}
public String toString() {
return getClass().getName();
}
}