Mean Shift Library

Abstract

Mean shift is usually associated, in computer vision at least, with the segmentation of an image. Whilst this library supports that scenario, it is far more general. Mean shift is a gradient ascent method for finding the modes of a kernel density estimate, so this library is as much a kernel density estimation library as it is a mode finder. It includes the usual kernel bandwidth estimation methods and also supports subspace constrained mean shift, which finds edges/manifolds in noisy data. Support goes far beyond the typical Gaussian and Uniform kernels: It has ten kernel types, as well as the ability to combine them, with different kernels on different parts of a feature vector. The kernels include directional distributions, so it supports density estimation over the position and orientation of an object, for instance. It also supports the multiplication of density estimates, which allows you to perform non-parametric belief propagation using mean shift objects as the messages between random variables. Also has methods to approximate values such as the entropy of a density estimate, and the KL divergence between two estimates.

Papers that are implemented, in all or in part, by this library include:

• E. A. Nadaraya, “On estimating regression”, Theory of Probability & Its Applications, 1964.
• G. S. Watson, “Smooth regression analysis”, Sankhya: The Indian J. of Statistics, Series A, 1964.
• K. Fukunaga, and L. D. Hostetler, “The Estimation of the Gradient of a Density Function”, with Applications in Pattern Recognition. Trans. on Information Theory, 1975.
• B. W. Silverman, “Density estimation for statistics and data analysis”, Monographs on Statistics and Applied Probability, 1986.
• P. Sarda, “Smoothing parameter selection for smooth distribution functions”, J. of Statistical Planning and Inference, 1993.
• D. Comaniciu, and P. Meer, “Mean Shift: A Robust Approach Toward Feature Space Analysis”, Trans. on Pattern Analysis and Machine Intelligence, 2002.
• E. B. Sudderth, A. T. Ihler, W. T. Freeman, and A. S. Willsky, “Nonparametric belief propagation”, Computer Vision and Pattern Recognition, 2003.
• J. M. Saragih, S. Lucey and J. F. Cohn. “Face alignment through subspace constrained mean-shifts”, Int. Conf. of Computer Vision, 2009.

Citation Style:    Display Alpha ACM SIGGRAPH IEEEtran Publication

Mean Shift Library. Tom S. F. Haines. GitHub repository https://github.com/thaines/helit/tree/master/ms, Feburary 2016.Tom S. F. Haines. Mean shift library. GitHub repository https://github.com/thaines/helit/tree/master/ms, February 2016.Haines, T. S. F., 2016. Mean shift library.GitHub repositoryhttps://github.com/thaines/helit/tree/master/ms, Feb.T. S. F. Haines, “Mean shift library,” GitHub repository https://github.com/thaines/helit/tree/master/ms, Feb. 2016. BibTeX Github Repository

Acknowledgments

The author was partially supported by EPSRC grants EP/J021458/1 and EP/K023578/1.