Riemannian Analysis of Probability Density Functions with Applications in Vision

Joshi, S.; Srivastava, A.; Jermyn, I.H.

doi:10.1109/cvpr.2007.383188

Riemannian Analysis of Probability Density Functions with Applications in Vision

Joshi, S.; Srivastava, A.; Jermyn, I.H.

Authors

S. Joshi

A. Srivastava

Professor Ian Jermyn i.h.jermyn@durham.ac.uk
Professor

Abstract

Applications in computer vision involve statistically analyzing an important class of constrained, nonnegative functions, including probability density functions (in texture analysis), dynamic time-warping functions (in activity analysis), and re-parametrization or non-rigid registration functions (in shape analysis of curves). For this one needs to impose a Riemannian structure on the spaces formed by these functions. We propose a "spherical" version of the Fisher-Rao metric that provides closed-form expressions for geodesics and distances, and allows fast computation of sample statistics. To demonstrate this approach, we present an application in planar shape classification.

Citation

Joshi, S., Srivastava, A., & Jermyn, I. (2007). Riemannian Analysis of Probability Density Functions with Applications in Vision. In 2007 IEEE Conference on Computer Vision and Pattern Recognition ; proceedings (1664-1671). https://doi.org/10.1109/cvpr.2007.383188

Conference Name	IEEE Conference on Computer Vision and Pattern Recognition 2007
Conference Location	Minneapolis
Publication Date	Jun 1, 2007
Deposit Date	Aug 12, 2011
Publicly Available Date	Apr 20, 2016
Pages	1664-1671
Series ISSN	1063-6919
Book Title	2007 IEEE Conference on Computer Vision and Pattern Recognition ; proceedings
DOI	https://doi.org/10.1109/cvpr.2007.383188

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