HistogramTools (version 0.3.2)

HistogramDistance: Histogram Distance Measures

Description

The pairs of bins in two histograms with the same bucket boundaries are compared to compute dissimilarity measures.

Usage

minkowski.dist(h1, h2, p)
intersect.dist(h1, h2)
kl.divergence(h1, h2)
jeffrey.divergence(h1, h2)

Arguments

h1,h2

"histogram" objects (created by hist) representing a binned dataset.

p

Order of the Minkowski distance between two histograms to compute.

Details

The minkowski.dist function computes the Minkowski distance of order p between two histograms. p=1 is the Manhattan distance and p=2 is the Euclidean distance.

The intersect.dist function computes the intersection distance of two histograms, as defined in Swain and Ballard 1991, p15. If histograms h1 and h2 do not contain the same total of counts, then this metric will not be symmetric.

The kl.divergence function computes the Kullback-Leibler divergence between two histograms.

The jeffrey.divergence function computes the Jeffrey divergence between two histograms.

References

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.

Puzicha, Jan, Thomas Hofmann, and Joachim M. Buhmann. "Non-parametric similarity measures for unsupervised texture segmentation and image retrieval." Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on. IEEE, 1997.

Swain, Michael J., and Dana H. Ballard. "Color indexing." International journal of computer vision 7.1 (1991): 11-32.

See Also

histogramtools-package, ecdf, and hist.

Examples

h1 <- hist(runif(100), plot=FALSE)
h2 <- hist(runif(100), plot=FALSE)

minkowski.dist(h1, h2, 1)
minkowski.dist(h1, h2, 2)
minkowski.dist(h1, h2, 3)
intersect.dist(h1, h2)
kl.divergence(h1, h2)
jeffrey.divergence(h1, h2)