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Proceedings Paper

Least statistically dependent basis and its application to image modeling
Author(s): Naoki Saito
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Paper Abstract

Statistical independence is one of the most desirable properties for a coordinate system for representing and modeling images. In reality, however, truly independent coordinates may not exist for a given set of images, or it may be computationally too difficult to obtain such coordinates. Therefore, it makes sense to obtain the least statistically dependent coordinate system efficiently. This basis--we call it Least Statistically-Dependent Basis (LSDB)--can be rapidly computed by minimizing the sum of the differential entropy of each coordinate in the basis library. This criterion is quite different from the Joint Best Basis (JBB) proposed by Wickerhauser. We demonstrate the use of the LSDB for image modeling and compare its performance with JBB and Karhunen-Loeve Basis.

Paper Details

Date Published: 19 October 1998
PDF: 14 pages
Proc. SPIE 3458, Wavelet Applications in Signal and Imaging Processing VI, (19 October 1998); doi: 10.1117/12.328146
Show Author Affiliations
Naoki Saito, Univ. of California/Davis (United States)


Published in SPIE Proceedings Vol. 3458:
Wavelet Applications in Signal and Imaging Processing VI
Andrew F. Laine; Michael A. Unser; Akram Aldroubi, Editor(s)

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