Java tutorial
/* * This file is part of the LIRE project: http://www.semanticmetadata.net/lire * 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 */ package net.semanticmetadata.lire.filter; import net.semanticmetadata.lire.ImageSearchHits; import net.semanticmetadata.lire.SearchHitsFilter; import net.semanticmetadata.lire.imageanalysis.LireFeature; import net.semanticmetadata.lire.impl.SimpleImageSearchHits; import net.semanticmetadata.lire.impl.SimpleResult; import net.semanticmetadata.lire.utils.MetricsUtils; import org.apache.commons.math3.linear.Array2DRowRealMatrix; import org.apache.commons.math3.linear.RealMatrix; import org.apache.commons.math3.linear.SingularValueDecomposition; import org.apache.lucene.document.Document; import org.apache.lucene.index.IndexReader; import org.apache.lucene.search.ScoreDoc; import org.apache.lucene.search.TopDocs; import java.io.IOException; import java.util.Iterator; import java.util.LinkedList; import java.util.TreeSet; import java.util.logging.Logger; /** * Filtering results based on LSA. * Created 05.08.11, 08:41 <br/> * * @author Mathias Lux, mathias@juggle.at */ public class LsaFilter implements SearchHitsFilter { private Logger logger = Logger.getLogger(getClass().getName()); private Class featureClass; private String fieldName; private LireFeature tempFeature = null; private int numberOfDimensions = -1; public LsaFilter(Class featureClass, String fieldName) { this.featureClass = featureClass; this.fieldName = fieldName; } /** * @param results * @param query * @return the filtered results or null if error occurs. */ public ImageSearchHits filter(ImageSearchHits results, Document query) { // create a double[items][histogram] tempFeature = null; LinkedList<double[]> features = new LinkedList<double[]>(); try { tempFeature = (LireFeature) featureClass.newInstance(); } catch (Exception e) { logger.severe("Could not create feature " + featureClass.getName() + " (" + e.getMessage() + ")."); return null; } // get all features from the result set, take care of those that do not have the respective field. for (int i = 0; i < results.length(); i++) { Document d = results.doc(i); if (d.getField(fieldName) != null) { tempFeature.setByteArrayRepresentation(d.getField(fieldName).binaryValue().bytes, d.getField(fieldName).binaryValue().offset, d.getField(fieldName).binaryValue().length); features.add(tempFeature.getDoubleHistogram()); } } // now go for the query if (query.getField(fieldName) != null) { tempFeature.setByteArrayRepresentation(query.getField(fieldName).binaryValue().bytes, query.getField(fieldName).binaryValue().offset, query.getField(fieldName).binaryValue().length); } else { logger.severe("Query document is missing the given feature " + featureClass.getName() + "."); return null; } double[][] matrixData = new double[features.size() + 1][tempFeature.getDoubleHistogram().length]; System.arraycopy(tempFeature.getDoubleHistogram(), 0, matrixData[0], 0, tempFeature.getDoubleHistogram().length); int count = 1; for (Iterator<double[]> iterator = features.iterator(); iterator.hasNext();) { double[] next = iterator.next(); System.arraycopy(next, 0, matrixData[count], 0, next.length); count++; } for (int i = 0; i < matrixData.length; i++) { double[] doubles = matrixData[i]; for (int j = 0; j < doubles.length; j++) { if (Double.isNaN(doubles[j])) System.err.println("Value is NaN"); ; } } // create a matrix object and do the magic Array2DRowRealMatrix m = new Array2DRowRealMatrix(matrixData); long ms = System.currentTimeMillis(); SingularValueDecomposition svd = new SingularValueDecomposition(m); ms = System.currentTimeMillis() - ms; double[] singularValues = svd.getSingularValues(); RealMatrix s = svd.getS(); // if no number of dimensions is given reduce to a tenth. if (numberOfDimensions < 1) numberOfDimensions = singularValues.length / 10; for (int i = numberOfDimensions; i < singularValues.length; i++) { s.setEntry(i, i, 0); } RealMatrix mNew = svd.getU().multiply(s).multiply(svd.getVT()); double[][] data = mNew.getData(); // create the new result set TreeSet<SimpleResult> result = new TreeSet<SimpleResult>(); double maxDistance = 0; double[] queryData = data[0]; for (int i = 1; i < data.length; i++) { double[] doubles = data[i]; double distance = MetricsUtils.distL1(doubles, queryData); result.add(new SimpleResult((float) distance, results.doc(i - 1), i - 1)); maxDistance = Math.max(maxDistance, distance); } ImageSearchHits hits; hits = new SimpleImageSearchHits(result, (float) maxDistance); return hits; } public ImageSearchHits filter(TopDocs results, IndexReader reader, Document query) throws IOException { LinkedList<SimpleResult> tmp = new LinkedList<SimpleResult>(); double max = 0; for (int i = 0; i < results.scoreDocs.length; i++) { ScoreDoc scoreDoc = results.scoreDocs[i]; SimpleResult s = new SimpleResult(1 / scoreDoc.score, reader.document(scoreDoc.doc), scoreDoc.doc); max = Math.max(max, 1 / scoreDoc.score); tmp.add(s); } return filter(new SimpleImageSearchHits(tmp, (float) max), query); } }