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Proceedings Paper

Gabor filter subset selection using a genetic algorithm
Author(s): Clelia Mandriota; Nicola Ancona; Ettore Stella; Arcangelo Distante
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Paper Abstract

This paper introduces a hybrid methodology that ensemble genetic algorithms and Support Vector Machine (SVM) in order to evolve optimal subsets of Gabor filters for efficient pattern classification. ALthough some filter design procedure are available for Gabor filters, high computations are needed and the efficiency of design is dependent on the particualr Gabor filter subset. In this paper to reduce the computational cost and improve the performance, a GA is used to search the space of all possible subsets of a large pool of Gabor candidate filters. The classification performance of SVM, an unknown data, together with filtering cost are used as measure of fitness that is used as feedback by GA to evolve better Gabor filter sets. This assembled system iterates until filters subset is found with a satisfactory classification performance and a significant reduced filters number.

Paper Details

Date Published: 18 October 2002
PDF: 8 pages
Proc. SPIE 4902, Optomechatronic Systems III, (18 October 2002); doi: 10.1117/12.467680
Show Author Affiliations
Clelia Mandriota, Institute of Intelligent Systems for Automation/CNR (Italy)
Nicola Ancona, Institute of Intelligent Systems for Automation/CNR (Italy)
Ettore Stella, Institute of Intelligent Systems for Automation/CNR (Italy)
Arcangelo Distante, Institute of Intelligent Systems for Automation/CNR (Italy)


Published in SPIE Proceedings Vol. 4902:
Optomechatronic Systems III
Toru Yoshizawa, Editor(s)

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