package de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2011
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable;
import de.lmu.ifi.dbs.elki.database.query.distance.SpatialDistanceQuery;
import de.lmu.ifi.dbs.elki.database.query.distance.SpatialPrimitiveDistanceQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractVectorDoubleDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.SpatialPrimitiveDoubleDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Intersection distance for color histograms.
*
* According to: M. J. Swain, D. H. Ballard:<br />
* Color indexing<br />
* International Journal of Computer Vision, 7(1), 32, 1991
*
* @author Erich Schubert
*/
@Title("Color histogram intersection distance")
@Description("Distance function for color histograms that emphasizes 'strong' bins.")
@Reference(authors = "M. J. Swain, D. H. Ballard", title = "Color Indexing", booktitle = "International Journal of Computer Vision, 7(1), 32, 1991")
public class HistogramIntersectionDistanceFunction extends AbstractVectorDoubleDistanceFunction implements SpatialPrimitiveDoubleDistanceFunction<NumberVector<?, ?>> {
/**
* Static instance
*/
public static final HistogramIntersectionDistanceFunction STATIC = new HistogramIntersectionDistanceFunction();
/**
* Constructor. No parameters.
*
* @deprecated Use static instance
*/
@Deprecated
public HistogramIntersectionDistanceFunction() {
super();
}
@Override
public DoubleDistance minDist(SpatialComparable mbr1, SpatialComparable mbr2) {
return new DoubleDistance(doubleMinDist(mbr1, mbr2));
}
@Override
public DoubleDistance centerDistance(SpatialComparable mbr1, SpatialComparable mbr2) {
return new DoubleDistance(doubleCenterDistance(mbr1, mbr2));
}
@Override
public double doubleDistance(NumberVector<?, ?> v1, NumberVector<?, ?> v2) {
final int dim1 = v1.getDimensionality();
if(dim1 != v2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of FeatureVectors" + "\n first argument: " + v1.toString() + "\n second argument: " + v2.toString() + "\n" + v1.getDimensionality() + "!=" + v2.getDimensionality());
}
double dist = 0;
double norm1 = 0;
double norm2 = 0;
for(int i = 1; i <= dim1; i++) {
final double val1 = v1.doubleValue(i);
final double val2 = v2.doubleValue(i);
dist += Math.min(val1, val2);
norm1 += val1;
norm2 += val2;
}
dist = 1 - dist / Math.min(norm1, norm2);
return dist;
}
@Override
public double doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) {
final int dim1 = mbr1.getDimensionality();
if(dim1 != mbr2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of FeatureVectors" + "\n first argument: " + mbr1.toString() + "\n second argument: " + mbr2.toString() + "\n" + mbr1.getDimensionality() + "!=" + mbr2.getDimensionality());
}
double dist = 0;
double norm1 = 0;
double norm2 = 0;
for(int i = 1; i <= dim1; i++) {
final double min1 = mbr1.getMin(i);
final double max1 = mbr1.getMax(i);
final double min2 = mbr2.getMin(i);
final double max2 = mbr2.getMax(i);
dist += Math.min(max1, max2);
norm1 += min1;
norm2 += min2;
}
dist = 1 - dist / Math.min(norm1, norm2);
return dist;
}
@Override
public double doubleCenterDistance(SpatialComparable mbr1, SpatialComparable mbr2) {
final int dim1 = mbr1.getDimensionality();
if(dim1 != mbr2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of FeatureVectors" + "\n first argument: " + mbr1.toString() + "\n second argument: " + mbr2.toString() + "\n" + mbr1.getDimensionality() + "!=" + mbr2.getDimensionality());
}
double dist = 0;
double norm1 = 0;
double norm2 = 0;
for(int i = 1; i <= dim1; i++) {
final double val1 = (mbr1.getMin(i) + mbr1.getMax(i)) / 2;
final double val2 = (mbr2.getMin(i) + mbr2.getMax(i)) / 2;
dist += Math.min(val1, val2);
norm1 += val1;
norm2 += val2;
}
dist = 1 - dist / Math.min(norm1, norm2);
return dist;
}
@Override
public <T extends NumberVector<?, ?>> SpatialDistanceQuery<T, DoubleDistance> instantiate(Relation<T> relation) {
return new SpatialPrimitiveDistanceQuery<T, DoubleDistance>(relation, this);
}
@Override
public String toString() {
return "HistogramIntersectionDistance";
}
@Override
public boolean equals(Object obj) {
if(obj == null) {
return false;
}
return this.getClass().equals(obj.getClass());
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected HistogramIntersectionDistanceFunction makeInstance() {
return HistogramIntersectionDistanceFunction.STATIC;
}
}
}