net.semanticmetadata.lire.searchers.custom.TopDocsImageSearcher Maven / Gradle / Ivy
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/*
* 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 ([email protected])
* http://www.semanticmetadata.net/lire, http://www.lire-project.net
*/
package net.semanticmetadata.lire.searchers.custom;
import net.semanticmetadata.lire.builders.DocumentBuilder;
import net.semanticmetadata.lire.imageanalysis.features.GlobalFeature;
import net.semanticmetadata.lire.searchers.ImageSearchHits;
import net.semanticmetadata.lire.searchers.SimpleImageSearchHits;
import net.semanticmetadata.lire.searchers.SimpleResult;
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.search.TopDocs;
import org.apache.lucene.util.Bits;
import java.awt.image.BufferedImage;
import java.io.IOException;
import java.util.TreeSet;
import java.util.logging.Level;
import java.util.logging.Logger;
/**
* This file is part of the Caliph and Emir project: http://www.SemanticMetadata.net
*
Date: 01.02.2006
*
Time: 00:17:02
*
* TODO: revisit for performance, feature caching, etc.
*
* @author Mathias Lux, [email protected]
*/
public class TopDocsImageSearcher {
protected Logger logger = Logger.getLogger(getClass().getName());
Class descriptorClass;
String fieldName;
private int maxHits = 10;
protected TreeSet docs;
public TopDocsImageSearcher(int maxHits, Class descriptorClass, String fieldName) {
this.maxHits = maxHits;
docs = new TreeSet();
this.descriptorClass = descriptorClass;
this.fieldName = fieldName;
}
public ImageSearchHits search(BufferedImage image, IndexReader reader, TopDocs results) throws IOException {
logger.finer("Starting extraction.");
GlobalFeature globalFeature = null;
SimpleImageSearchHits searchHits = null;
try {
globalFeature = (GlobalFeature) descriptorClass.newInstance();
// 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(results, reader, globalFeature);
searchHits = new SimpleImageSearchHits(this.docs, maxDistance);
} catch (InstantiationException e) {
logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
} catch (IllegalAccessException e) {
logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
}
return searchHits;
}
/**
* @param results
* @param reader
* @param globalFeature
* @return the maximum distance found for normalizing.
* @throws java.io.IOException
*/
protected double findSimilar(TopDocs results, IndexReader reader, GlobalFeature globalFeature) throws IOException {
double maxDistance = -1d, overallMaxDistance = -1d;
boolean hasDeletions = reader.hasDeletions();
// clear result set ...
docs.clear();
// Needed for check whether the document is deleted.
Bits liveDocs = MultiFields.getLiveDocs(reader);
int docs = results.totalHits;
for (int i = 0; i < docs; i++) {
if (reader.hasDeletions() && !liveDocs.get(i)) continue; // if it is deleted, just ignore it.
Document d = reader.document(results.scoreDocs[i].doc);
double distance = getDistance(d, globalFeature);
assert (distance >= 0);
// calculate the overall max distance to normalize score afterwards
if (overallMaxDistance < distance) {
overallMaxDistance = distance;
}
// if it is the first document:
if (maxDistance < 0) {
maxDistance = distance;
}
// if the array is not full yet:
if (this.docs.size() < maxHits) {
this.docs.add(new SimpleResult(distance, results.scoreDocs[i].doc));
if (distance > maxDistance) maxDistance = distance;
} else if (distance < 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(distance, results.scoreDocs[i].doc));
// and set our new distance border ...
maxDistance = this.docs.last().getDistance();
}
}
return maxDistance;
}
protected double getDistance(Document d, GlobalFeature globalFeature) {
double distance = 0d;
GlobalFeature lf;
try {
lf = (GlobalFeature) descriptorClass.newInstance();
lf.setByteArrayRepresentation(d.getField(fieldName).binaryValue().bytes, d.getField(fieldName).binaryValue().offset, d.getField(fieldName).binaryValue().length);
distance = globalFeature.getDistance(lf);
} catch (InstantiationException e) {
logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
} catch (IllegalAccessException e) {
logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
}
return distance;
}
public ImageSearchHits search(TopDocs results, Document d, IndexReader reader) throws IOException {
SimpleImageSearchHits searchHits = null;
try {
GlobalFeature lf;// = (GlobalFeature) descriptorClass.newInstance();
lf = (GlobalFeature) descriptorClass.newInstance();
lf.setByteArrayRepresentation(d.getField(fieldName).binaryValue().bytes, d.getField(fieldName).binaryValue().offset, d.getField(fieldName).binaryValue().length);
double maxDistance = findSimilar(results, reader, lf);
searchHits = new SimpleImageSearchHits(this.docs, maxDistance);
} catch (InstantiationException e) {
logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
} catch (IllegalAccessException e) {
logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
}
return searchHits;
}
public String toString() {
return "TopDocsImageSearcher using " + descriptorClass.getName();
}
}
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