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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
*
* Updated: 26.08.14 13:20
*/
package net.semanticmetadata.lire.searchers;
import net.semanticmetadata.lire.builders.DocumentBuilder;
import net.semanticmetadata.lire.imageanalysis.features.GlobalFeature;
import net.semanticmetadata.lire.indexers.hashing.BitSampling;
import org.apache.lucene.document.Document;
import org.apache.lucene.index.FieldInvertState;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.Term;
import org.apache.lucene.search.*;
import org.apache.lucene.search.similarities.DefaultSimilarity;
import java.awt.image.BufferedImage;
import java.io.IOException;
import java.io.InputStream;
import java.util.TreeSet;
/**
* This class allows for searching based on {@link net.semanticmetadata.lire.indexers.hashing.BitSampling}
* HashingMode. First a number of candidates is retrieved from the index, then the candidates are re-ranked.
* The number of candidates can be tuned with the numHashedResults parameter in the constructor. The higher
* this parameter, the better the results, but the slower the search.
*
* @author Mathias Lux, [email protected], 2013-04-12
*/
public class BitSamplingImageSearcher extends AbstractImageSearcher {
private int maxResultsHashBased = 1000;
private int maximumHits = 100;
private String featureFieldName = null;
private GlobalFeature feature = null;
private String hashesFieldName = null;
private boolean partialHashes = false;
/**
* Creates a new searcher for BitSampling based hashes.
*
* @param maximumHits how many hits the searcher shall return.
* @param featureFieldName the field hashFunctionsFileName of the feature.
* @param hashesFieldName the field hashFunctionsFileName of the hashes.
* @param feature an instance of the feature.
*/
public BitSamplingImageSearcher(int maximumHits, String featureFieldName, String hashesFieldName, GlobalFeature feature) {
this.maximumHits = maximumHits;
this.featureFieldName = featureFieldName;
this.hashesFieldName = hashesFieldName;
this.feature = feature;
try {
BitSampling.readHashFunctions();
} catch (IOException e) {
System.err.println("Error reading hash functions from default location.");
e.printStackTrace();
}
}
/**
* Creates a new searcher for BitSampling based hashes. The field names are inferred from the entries in //TODO {@ link GenericDocumentBuilder}
*
* @param maximumHits how many hits the searcher shall return.
* @param feature an instance of the feature.
*/
public BitSamplingImageSearcher(int maximumHits, GlobalFeature feature) {
this.maximumHits = maximumHits;
this.featureFieldName = feature.getFieldName();
this.hashesFieldName = featureFieldName + DocumentBuilder.HASH_FIELD_SUFFIX;
this.feature = feature;
try {
BitSampling.readHashFunctions();
} catch (IOException e) {
System.err.println("Error reading hash functions from default location.");
e.printStackTrace();
}
}
/**
* Creates a new searcher for BitSampling based hashes. The field names are inferred from the entries in //TODO {@ link GenericDocumentBuilder}
*
* @param maximumHits how many hits the searcher shall return.
* @param feature an instance of the feature.
* @param useFastSearch if true it only uses a random sample of hashes for the query and speeds up the search significantly.
*/
public BitSamplingImageSearcher(int maximumHits, GlobalFeature feature, boolean useFastSearch) {
this.maximumHits = maximumHits;
featureFieldName = feature.getFieldName();
hashesFieldName = featureFieldName + DocumentBuilder.HASH_FIELD_SUFFIX;
this.feature = feature;
partialHashes = useFastSearch;
try {
BitSampling.readHashFunctions();
} catch (IOException e) {
System.err.println("Error reading hash functions from default location.");
e.printStackTrace();
}
}
/**
* Creates a new searcher for BitSampling based hashes. The field names are inferred from the entries in //TODO {@ link GenericDocumentBuilder}
*
* @param maximumHits how many hits the searcher shall return.
* @param feature an instance of the feature.
* @param numHashedResults the number of candidate results retrieved from the index before re-ranking.
*/
public BitSamplingImageSearcher(int maximumHits, GlobalFeature feature, int numHashedResults) {
this.maximumHits = maximumHits;
this.featureFieldName = feature.getFieldName();
this.hashesFieldName = featureFieldName + DocumentBuilder.HASH_FIELD_SUFFIX;
this.feature = feature;
maxResultsHashBased = numHashedResults;
try {
BitSampling.readHashFunctions();
} catch (IOException e) {
System.err.println("Error reading hash functions from default location.");
e.printStackTrace();
}
}
public BitSamplingImageSearcher(int maximumHits, String featureFieldName, String hashesFieldName, GlobalFeature feature, int numHashedResults) {
this.maximumHits = maximumHits;
this.featureFieldName = featureFieldName;
this.hashesFieldName = hashesFieldName;
this.feature = feature;
maxResultsHashBased = numHashedResults;
try {
BitSampling.readHashFunctions();
} catch (IOException e) {
System.err.println("Error reading hash functions from default location.");
e.printStackTrace();
}
}
public BitSamplingImageSearcher(int maximumHits, String featureFieldName, String hashesFieldName, GlobalFeature feature, InputStream hashes) {
this.maximumHits = maximumHits;
this.featureFieldName = featureFieldName;
this.hashesFieldName = hashesFieldName;
this.feature = feature;
try {
BitSampling.readHashFunctions(hashes);
hashes.close();
} catch (IOException e) {
System.err.println("Error reading has functions from given input stream.");
e.printStackTrace();
}
}
public BitSamplingImageSearcher(int maximumHits, String featureFieldName, String hashesFieldName, GlobalFeature feature, InputStream hashes, int numHashedResults) {
this.maximumHits = maximumHits;
this.featureFieldName = featureFieldName;
this.hashesFieldName = hashesFieldName;
this.feature = feature;
maxResultsHashBased = numHashedResults;
try {
BitSampling.readHashFunctions(hashes);
hashes.close();
} catch (IOException e) {
System.err.println("Error reading has functions from given input stream.");
e.printStackTrace();
}
}
public ImageSearchHits search(BufferedImage image, IndexReader reader) throws IOException {
try {
GlobalFeature queryFeature = feature.getClass().newInstance();
queryFeature.extract(image);
int[] ints = BitSampling.generateHashes(queryFeature.getFeatureVector());
String[] hashes = new String[ints.length];
for (int i = 0; i < ints.length; i++) {
hashes[i] = Integer.toString(ints[i]);
}
return search(hashes, queryFeature, reader);
} catch (Exception e) {
e.printStackTrace();
}
return null;
}
public ImageSearchHits search(Document doc, IndexReader reader) throws IOException {
try {
GlobalFeature queryFeature = feature.getClass().newInstance();
queryFeature.setByteArrayRepresentation(doc.getBinaryValue(featureFieldName).bytes,
doc.getBinaryValue(featureFieldName).offset,
doc.getBinaryValue(featureFieldName).length);
return search(doc.getValues(hashesFieldName)[0].split(" "), queryFeature, reader);
// return search(doc.getValues(hashesFieldName + "_q")[0].split(" "), queryFeature, reader); // just for debug if a query feature is stored in the index.
} catch (Exception e) {
e.printStackTrace();
}
return null;
}
private ImageSearchHits search(String[] hashes, GlobalFeature queryFeature, IndexReader reader) throws IOException {
// first search by text:
IndexSearcher searcher = new IndexSearcher(reader);
searcher.setSimilarity(new BaseSimilarity());
BooleanQuery query = new BooleanQuery();
for (int i = 0; i < hashes.length; i++) {
// be aware that the hashFunctionsFileName of the field must match the one you put the hashes in before.
if (partialHashes) {
if (Math.random() < 0.5)
query.add(new BooleanClause(new TermQuery(new Term(hashesFieldName, hashes[i] + "")), BooleanClause.Occur.SHOULD));
} else
query.add(new BooleanClause(new TermQuery(new Term(hashesFieldName, hashes[i] + "")), BooleanClause.Occur.SHOULD));
}
TopDocs docs = searcher.search(query, maxResultsHashBased);
// System.out.println(docs.totalHits);
// then re-rank
TreeSet resultScoreDocs = new TreeSet();
double maxDistance = -1d;
double tmpScore;
for (int i = 0; i < docs.scoreDocs.length; i++) {
feature.setByteArrayRepresentation(reader.document(docs.scoreDocs[i].doc).getBinaryValue(featureFieldName).bytes,
reader.document(docs.scoreDocs[i].doc).getBinaryValue(featureFieldName).offset,
reader.document(docs.scoreDocs[i].doc).getBinaryValue(featureFieldName).length);
tmpScore = queryFeature.getDistance(feature);
assert (tmpScore >= 0);
if (resultScoreDocs.size() < maximumHits) {
resultScoreDocs.add(new SimpleResult(tmpScore, docs.scoreDocs[i].doc));
maxDistance = Math.max(maxDistance, tmpScore);
} else if (tmpScore < maxDistance) {
// if it is nearer to the sample than at least one of the current set:
// remove the last one ...
resultScoreDocs.remove(resultScoreDocs.last());
// add the new one ...
resultScoreDocs.add(new SimpleResult(tmpScore, docs.scoreDocs[i].doc));
// and set our new distance border ...
maxDistance = resultScoreDocs.last().getDistance();
}
}
assert (resultScoreDocs.size() <= maximumHits);
return new SimpleImageSearchHits(resultScoreDocs, maxDistance);
}
public ImageDuplicates findDuplicates(IndexReader reader) throws IOException {
throw new UnsupportedOperationException("not implemented.");
}
class BaseSimilarity extends DefaultSimilarity {
public float tf(float freq) {
return freq;
}
public float idf(long docFreq, long numDocs) {
return 1;
}
public float coord(int overlap, int maxOverlap) {
return 1;
}
public float queryNorm(float sumOfSquaredWeights) {
return 1;
}
public float sloppyFreq(int distance) {
return 1;
}
public float lengthNorm(FieldInvertState state) {
return 1;
}
}
}
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