/* * 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 */ package net.semanticmetadata.lire.aggregators; import net.semanticmetadata.lire.classifiers.Cluster; import net.semanticmetadata.lire.imageanalysis.features.LocalFeature; import net.semanticmetadata.lire.utils.SerializationUtils; import java.util.Arrays; import java.util.List; /** * General class creating vector representations based on VLAD model, given the list of local features and the codebook. * Created by Nektarios on 03/06/2015. * * @author Nektarios Anagnostopoulos, nek.anag@gmail.com * (c) 2015 by Nektarios Anagnostopoulos */ public class VLAD extends AbstractAggregator { private double[] vector; public VLAD() { } /** * Given a list of features and a codebook, {@link VLAD#createVectorRepresentation(List, Cluster[])} aggregates * the features to create the vector representation according to the VLAD model. * @param listOfLocalFeatures is the list of features. * @param clustersArray is the codebook. */ @Override public void createVectorRepresentation(List<? extends LocalFeature> listOfLocalFeatures, Cluster[] clustersArray) { vector = new double[clustersArray.length * (clustersArray[0].getMean()).length]; Arrays.fill(vector, 0d); int clusterIndex; double[] mean; // VLAD - Vector of Locally Aggregated Descriptors for (LocalFeature localFeature : listOfLocalFeatures) { clusterIndex = clusterForFeature(localFeature.getFeatureVector(), clustersArray); mean = clustersArray[clusterIndex].getMean(); for (int i = 0; i < localFeature.getFeatureVector().length; i++) { vector[clusterIndex * localFeature.getFeatureVector().length + i] += (localFeature.getFeatureVector()[i] - mean[i]); } } normalize(vector); } /** * Returns the vector representation in byte[] format. * @return the vector representation as a byte array. */ @Override public byte[] getByteVectorRepresentation() { return SerializationUtils.toByteArray(vector); } /** * Returns the vector representation in string format. * @return the vector representation as string. */ @Override public String getStringVectorRepresentation() { return SerializationUtils.toString(vector); } /** * Returns the vector representation in double[] format. * @return the vector representation as a double array. */ @Override public double[] getVectorRepresentation() { return vector; } @Override public String getFieldName() { return Aggregator.FIELD_NAME_VLAD; } /** * Returns the vector representation in string format. * @return the vector representation as string. */ public String toString() { return getStringVectorRepresentation();} private void normalize(double[] histogram) { // L2 double sumOfSquares = 0; for (double next : histogram) { sumOfSquares += (next * next); } if (sumOfSquares > 0) { sumOfSquares = Math.sqrt(sumOfSquares); for (int i = 0; i < histogram.length; i++) { // histogram[i] = Math.floor(16d * histogram[i] / sumOfSquares); histogram[i] /= sumOfSquares; } } /* // L1 double min = Double.MAX_VALUE, max = Double.MIN_VALUE; for (int i = 0; i < histogram.length; i++) { min = Math.min(histogram[i], min); max = Math.max(histogram[i], max); } for (int i = 0; i < histogram.length; i++) { histogram[i] = (histogram[i] - min) / (max - min); }*/ } }