Ranking ICA bases by associative memory recalls of training texture samples

Mohammed Ameen; Pornchai Chanyagorn; Harold H. Szu

doi:10.1117/12.381714

5 April 2000 Ranking ICA bases by associative memory recalls of training texture samples

Mohammed Ameen, Pornchai Chanyagorn, Harold H. Szu

Proceedings Volume 4056, Wavelet Applications VII; (2000) https://doi.org/10.1117/12.381714
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

Mohammed Ameen, Pornchai Chanyagorn, and Harold H. Szu "Ranking ICA bases by associative memory recalls of training texture samples", Proc. SPIE 4056, Wavelet Applications VII, (5 April 2000); https://doi.org/10.1117/12.381714

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