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Proceedings Paper

Segmentation of multivariate mixed data via lossy coding and compression
Author(s): Harm Derksen; Yi Ma; Wei Hong; John Wright
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Paper Abstract

In this paper, based on ideas from lossy data coding and compression, we present a simple but surprisingly effective technique for segmenting multivariate mixed data that are drawn from a mixture of Gaussian distributions or linear subspaces. The goal is to find the optimal segmentation that minimizes the overall coding length of the segmented data, subject to a given distortion. We show that deterministic segmentation minimizes an upper bound on the (asymptotically) optimal solution. The proposed algorithm does not require any prior knowledge of the number or dimension of the groups, nor does it involve any parameter estimation. Simulation results reveal intriguing phase-transition behaviors of the number of segments when changing the level of distortion or the amount of outliers. Finally, we demonstrate how this technique can be readily applied to segment real imagery and bioinformatic data.

Paper Details

Date Published: 29 January 2007
PDF: 12 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65080H (29 January 2007); doi: 10.1117/12.714912
Show Author Affiliations
Harm Derksen, Univ. of Michigan (United States)
Yi Ma, Univ. of Illinois at Urbana Champaign (United States)
Wei Hong, Texas Instruments (United States)
John Wright, Univ. of Illinois at Urbana Champaign (United States)


Published in SPIE Proceedings Vol. 6508:
Visual Communications and Image Processing 2007
Chang Wen Chen; Dan Schonfeld; Jiebo Luo, Editor(s)

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