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Proceedings Paper

Ranking ICA bases by associative memory recalls of training texture samples
Author(s): Mohammed Ameen; Pornchai Chanyagorn; Harold H. Szu
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Paper Abstract

We wish to generalize the covariance matrix approach (PCA) by the statistical Independent Component Analyses (ICA), which have been implemented by Bell-Sejnowski efficiently using ANN methodology. The gain of the statistics is the los of the geometry. In this research, we preserve the texture geometry with a so-called local ICA, in order to extract separately independent features from each class of natural textures. To avoid the curse of the dimensionality due to the local ICA, we furthermore use the divide-and-conquer strategy. A single ICA basis vector is chosen from each texture class, based on the maximum associative recalls from the class training set. Subsequently, another ICA basis is chosen, if necessary, to minimize the false alarm rate, namely the spread of confusion matrix. For the visible remote sensing application, we have designed such an optimum classifier of all natural scene textures with a minimum spread of the confusion matrix.

Paper Details

Date Published: 5 April 2000
PDF: 10 pages
Proc. SPIE 4056, Wavelet Applications VII, (5 April 2000); doi: 10.1117/12.381714
Show Author Affiliations
Mohammed Ameen, George Washington Univ. (United States)
Pornchai Chanyagorn, George Washington Univ. (United States)
Harold H. Szu, Office of Naval Research (United States)

Published in SPIE Proceedings Vol. 4056:
Wavelet Applications VII
Harold H. Szu; Martin Vetterli; William J. Campbell; James R. Buss, Editor(s)

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