Description Usage Arguments Details Author(s) References See Also Examples
The pairs of bins in two histograms with the same bucket boundaries are compared to compute dissimilarity measures.
1 2 3 4 | minkowski.dist(h1, h2, p)
intersect.dist(h1, h2)
kl.divergence(h1, h2)
jeffrey.divergence(h1, h2)
|
h1,h2 |
|
p |
Order of the Minkowski distance between two histograms to compute. |
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.
Murray Stokely mstokely@google.com
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.
histogramtools-package
,
ecdf
, and
hist
.
1 2 3 4 5 6 7 8 9 | 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)
|
[1] 36
[1] 12.72792
[1] 9.412869
[1] 0.18
[1] 0.0930721
[1] 0.04934731
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