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Proceedings Paper

Deformable Bayesian networks for data clustering and fusion
Author(s): Kittipat Kampa; Jose C. Principe; J. Tory Cobb; Anand Rangarajan
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Paper Abstract

In this work, we propose DEformable BAyesian Networks (DEBAN), a probabilistic graphical model framework where model selection and statistical inference can be viewed as two key ingredients in the same iterative process. While this concept has shown successful results in computer vision community,1-4 our proposed approach generalizes the concept such that it is applicable to any data type. Our goal is to infer the optimal structure/model to fit the given observations. The optimal structure conveys an automatic way to find not only the number of clusters in the data set, but also the multiscale graph structure illustrating the dependence relationship among the variables in the network. Finally, the marginal posterior distribution at each root node is regarded as the fused information of its corresponding observations, and the most probable state can be found from the maximum a posteriori (MAP) solution with the uncertainty of the estimate in the form of a probability distribution which is desired for a variety of applications.

Paper Details

Date Published: 23 May 2011
PDF: 15 pages
Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 80170R (23 May 2011); doi: 10.1117/12.883096
Show Author Affiliations
Kittipat Kampa, Univ. of Florida (United States)
Jose C. Principe, Univ. of Florida (United States)
J. Tory Cobb, Naval Surface Warfare Ctr. Panama City Division (United States)
Anand Rangarajan, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 8017:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI
Russell S. Harmon; John H. Holloway Jr.; J. Thomas Broach, Editor(s)

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