/*
* 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 (mathias@juggle.at)
* http://www.semanticmetadata.net/lire, http://www.lire-project.net
*
* Updated: 07.08.13 12:18
*/
package net.semanticmetadata.lire.utils;
/**
* User: mlux
* Date: 25.11.2009
* Time: 14:32:49
*/
public class MetricsUtils {
/**
* Manhattan distance
*
* @param h1
* @param h2
* @return
*/
public static double distL1(int[] h1, int[] h2) {
assert (h1.length == h2.length);
double sum = 0d;
for (int i = 0; i < h1.length; i++) {
sum += Math.abs(h1[i] - h2[i]);
}
return sum / h1.length;
}
public static double distL1(double[] h1, double[] h2) {
assert (h1.length == h2.length);
double sum = 0d;
for (int i = 0; i < h1.length; i++) {
sum += Math.abs(h1[i] - h2[i]);
}
return sum / h1.length;
}
/**
* Euclidean distance
*
* @param h1
* @param h2
* @return
*/
public static double distL2(int[] h1, int[] h2) {
assert (h1.length == h2.length);
double sum = 0d;
for (int i = 0; i < h1.length; i++) {
sum += (h1[i] - h2[i]) * (h1[i] - h2[i]);
}
return Math.sqrt(sum);
}
/**
* Euclidean distance
*
* @param h1
* @param h2
* @return
*/
public static double distL2(double[] h1, double[] h2) {
// assert (h1.length == h2.length);
double sum = 0d;
for (int i = 0; i < h1.length; i++) {
sum += (h1[i] - h2[i]) * (h1[i] - h2[i]);
}
return Math.sqrt(sum);
}
/**
* Euclidean distance
*
* @param h1
* @param h2
* @return
*/
public static double distL2(float[] h1, float[] h2) {
assert (h1.length == h2.length);
double sum = 0d;
for (int i = 0; i < h1.length; i++) {
sum += (h1[i] - h2[i]) * (h1[i] - h2[i]);
}
return Math.sqrt(sum);
}
/**
* Jeffrey Divergence or Jensen-Shannon divergence (JSD) from
* Deselaers, T.; Keysers, D. & Ney, H. Features for image retrieval:
* an experimental comparison Inf. Retr., Kluwer Academic Publishers, 2008, 11, 77-107
*
* @param h1
* @param h2
* @return
*/
public static double jsd(int[] h1, int[] h2) {
assert (h1.length == h2.length);
double sum = 0d;
for (int i = 0; i < h1.length; i++) {
sum += (h1[i] > 0 ? h1[i] * Math.log(2d * h1[i] / (h1[i] + h2[i])) : 0) +
(h2[i] > 0 ? h2[i] * Math.log(2d * h2[i] / (h1[i] + h2[i])) : 0);
}
return sum;
}
/**
* Chi^2 statistics.
*
* @param d1
* @param d2
* @return distance like "unlikelihood"
*/
public static double chisquare(double[] d1, double[] d2) {
assert (d1.length == d2.length);
double sum = 0d;
double m;
for (int i = 0; i < d1.length; i++) {
m = (d1[i] + d2[i]) / 2;
sum += (d1[i] - m) * (d1[i] - m) / m;
}
return sum;
}
/**
* Earth Mover's Distance for two equal length, equal summed histograms as described in
* Rubner, Yossi, Carlo Tomasi, and Leonidas J. Guibas. "The earth mover's distance as a metric for image
* retrieval." International journal of computer vision 40.2 (2000): 99-121.
*
* @param d1 NOTE: sum(d1) needs to be equal to sum(d2)
* @param d2
* @return EMD
*/
public static double simpleEMD(double[] d1, double[] d2) {
assert (d1.length == d2.length);
double sum = 0d;
double m1 = 0, m2 = 0;
for (int i = 0; i < d1.length; i++) {
m1 += d1[i];
m2 += d2[i];
sum += Math.abs(m1 - m2);
}
return sum;
}
/**
* Kolmogorov-Smirnoff Distance for two equal length, equal summed histograms as described in
* Rubner, Yossi, Carlo Tomasi, and Leonidas J. Guibas. "The earth mover's distance as a metric for image
* retrieval." International journal of computer vision 40.2 (2000): 99-121.
*
* @param d1 NOTE: sum(d1) needs to be equal to sum(d2)
* @param d2
* @return EMD
*/
public static double ksDistance(double[] d1, double[] d2) {
assert (d1.length == d2.length);
double max = 0d;
double m1 = 0, m2 = 0;
for (int i = 0; i < d1.length; i++) {
m1 += d1[i];
m2 += d2[i];
max = Math.max(Math.abs(m1 - m2), max);
}
return max;
}
public static double jsd(byte[] h1, byte[] h2) {
assert (h1.length == h2.length);
double sum = 0d;
for (int i = 0; i < h1.length; i++) {
sum += (h1[i] > 0 ? h1[i] * Math.log(2d * h1[i] / (h1[i] + h2[i])) : 0) +
(h2[i] > 0 ? h2[i] * Math.log(2d * h2[i] / (h1[i] + h2[i])) : 0);
}
return sum;
}
public static double jsd(float[] h1, float[] h2) {
assert (h1.length == h2.length);
double sum = 0d;
for (int i = 0; i < h1.length; i++) {
sum += (h1[i] > 0 ? (h1[i] / 2d) * Math.log((2d * h1[i]) / (h1[i] + h2[i])) : 0) +
(h2[i] > 0 ? (h2[i] / 2d) * Math.log((2d * h2[i]) / (h1[i] + h2[i])) : 0);
}
return sum;
}
public static double jsd(double[] h1, double[] h2) {
assert (h1.length == h2.length);
double sum = 0d;
for (int i = 0; i < h1.length; i++) {
sum += (h1[i] > 0 ? (h1[i] / 2d) * Math.log((2d * h1[i]) / (h1[i] + h2[i])) : 0) +
(h2[i] > 0 ? (h2[i] / 2d) * Math.log((2d * h2[i]) / (h1[i] + h2[i])) : 0);
}
return sum;
}
public static double tanimoto(int[] h1, int[] h2) {
assert (h1.length == h2.length);
double result = 0d;
double tmp1 = 0d;
double tmp2 = 0d;
double tmpCnt1 = 0d, tmpCnt2 = 0d, tmpCnt3 = 0d;
for (int i = 0; i < h1.length; i++) {
tmp1 += h1[i];
tmp2 += h2[i];
}
if (tmp1 == 0 && tmp2 == 0) return 0;
if (tmp1 == 0 || tmp2 == 0) return 100;
if (tmp1 > 0 && tmp2 > 0) {
for (int i = 0; i < h1.length; i++) {
tmpCnt1 += (h1[i] / tmp1) * (h2[i] / tmp2);
tmpCnt2 += (h2[i] / tmp2) * (h2[i] / tmp2);
tmpCnt3 += (h1[i] / tmp1) * (h1[i] / tmp1);
}
result = (100 - 100 * (tmpCnt1 / (tmpCnt2 + tmpCnt3
- tmpCnt1))); //Tanimoto
}
return result;
}
public static double tanimoto(float[] h1, float[] h2) {
assert (h1.length == h2.length);
double result = 0d;
double tmp1 = 0d;
double tmp2 = 0d;
double tmpCnt1 = 0, tmpCnt2 = 0, tmpCnt3 = 0;
for (int i = 0; i < h1.length; i++) {
tmp1 += h1[i];
tmp2 += h2[i];
}
if (tmp1 == 0 && tmp2 == 0) return 0;
if (tmp1 == 0 || tmp2 == 0) return 100;
if (tmp1 > 0 && tmp2 > 0) {
for (int i = 0; i < h1.length; i++) {
tmpCnt1 += (h1[i] / tmp1) * (h2[i] / tmp2);
tmpCnt2 += (h2[i] / tmp2) * (h2[i] / tmp2);
tmpCnt3 += (h1[i] / tmp1) * (h1[i] / tmp1);
}
result = (100 - 100 * (tmpCnt1 / (tmpCnt2 + tmpCnt3
- tmpCnt1))); //Tanimoto
}
return result;
}
public static double tanimoto(double[] h1, double[] h2) {
assert (h1.length == h2.length);
double result = 0d;
double tmp1 = 0d;
double tmp2 = 0d;
double tmpCnt1 = 0, tmpCnt2 = 0, tmpCnt3 = 0;
for (int i = 0; i < h1.length; i++) {
tmp1 += h1[i];
tmp2 += h2[i];
}
if (tmp1 == 0 && tmp2 == 0) return 0;
if (tmp1 == 0 || tmp2 == 0) return 100;
if (tmp1 > 0 && tmp2 > 0) {
for (int i = 0; i < h1.length; i++) {
tmpCnt1 += (h1[i] / tmp1) * (h2[i] / tmp2);
tmpCnt2 += (h2[i] / tmp2) * (h2[i] / tmp2);
tmpCnt3 += (h1[i] / tmp1) * (h1[i] / tmp1);
}
result = (100 - 100 * (tmpCnt1 / (tmpCnt2 + tmpCnt3 - tmpCnt1))); //Tanimoto
}
return result;
}
public static double cosineCoefficient(double[] hist1, double[] hist2) {
assert (hist1.length == hist2.length);
double distance = 0d;
double tmp1 = 0d, tmp2 = 0d;
for (int i = 0; i < hist1.length; i++) {
distance += hist1[i] * hist2[i];
tmp1 += hist1[i] * hist1[i];
tmp2 += hist2[i] * hist2[i];
}
if (tmp1 * tmp2 > 0) {
return Math.max(0, (1d - distance / (Math.sqrt(tmp1) * Math.sqrt(tmp2))));
} else return 1d;
}
public static double distL1(float[] h1, float[] h2) {
assert (h1.length == h2.length);
double sum = 0d;
for (int i = 0; i < h1.length; i++) {
sum += Math.abs(h1[i] - h2[i]);
}
return sum;
}
public static double distL1(byte[] h1, byte[] h2) {
assert (h1.length == h2.length);
double sum = 0d;
for (int i = 0; i < h1.length; i++) {
sum += Math.abs(h1[i] - h2[i]);
}
return sum;
}
/**
* Max normalization of a double[] histogram.
*
* @param histogram
* @return
*/
public static double[] normalizeMax(double[] histogram) {
double[] result = new double[histogram.length];
double max = Double.MIN_VALUE;
double min = Double.MAX_VALUE;
for (int i = 0; i < histogram.length; i++) {
max = Math.max(max, histogram[i]);
min = Math.min(min, histogram[i]);
}
for (int i = 0; i < histogram.length; i++) {
result[i] = ((double) histogram[i] - min) / (max - min);
}
return result;
}
/**
* Euclidean normalization of a double[] histogram. // todo: make it faster and less memory consuming ...
*
* @param histogram
* @return
*/
public static double[] normalizeL2(double[] histogram) {
double[] result = new double[histogram.length];
double len = 0d;
for (int i = 0; i < histogram.length; i++) {
len += histogram[i] * histogram[i];
}
len = Math.sqrt(len);
for (int i = 0; i < histogram.length; i++) {
if (histogram[i] != 0)
result[i] = ((double) histogram[i]) / len;
else
result[i] = 0;
}
return result;
}
/**
* Euclidean normalization of a double[] histogram. // todo: make it faster and less memory consuming ...
*
* @param histogram
* @return
*/
public static double[] normalizeL1(double[] histogram) {
double[] result = new double[histogram.length];
double len = 0d;
for (int i = 0; i < histogram.length; i++) {
len += Math.abs(histogram[i]);
}
for (int i = 0; i < histogram.length; i++) {
result[i] = ((double) histogram[i]) / len;
}
return result;
}
}