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Proceedings Paper

Adaptive branch and bound algorithm (ABB) for use on hyperspectral data
Author(s): Songyot Nakariyakul; David P. Casasent
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Paper Abstract

We propose a new adaptive branch and bound (ABB) algorithm for selecting the optimal subset of features in hyperspectral applications. The algorithm improves the search speed by avoiding unnecessary criterion function calculations at nodes in the solution tree. Our algorithm includes the following new properties: (i) ordering the tree nodes by the significance of features during construction of the tree, (ii) obtaining a large "good" initial bound by a floating search method, (iii) a new method to select an initial starting search level in the tree, and (iv) a new adaptive jump search strategy to select subsequent search levels to avoid redundant criterion function calculations. Our experimental results for two databases demonstrate that our method is significantly faster than other versions of the branch and bound algorithm.

Paper Details

Date Published: 4 May 2006
PDF: 10 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62332E (4 May 2006); doi: 10.1117/12.666175
Show Author Affiliations
Songyot Nakariyakul, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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