NDLI: Evolutionary Gabor filter optimization with application to vehicle detection

Content Provider	IEEE Xplore Digital Library
Author	Sun, Z. Bebis, G. Miller, R.
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci., Nevada Univ., Reno, NV, USA (Sun, Z.; Bebis, G.)
Abstract	Despite the considerable amount of research work on the application of Gabor filters in pattern classification, their design and selection have been mostly done on a trial and error basis. Existing techniques are either only suitable for a small number of filters or less problem-oriented. A systematic and general evolutionary Gabor filter optimization (EGFO) approach that yields a more optimal, problem-specific, set of filters is proposed in this study. The EGFO approach unifies filter design with filter selection by integrating genetic algorithms (GAs) with an incremental clustering approach. Specifically, filter design is performed using GAs, a global optimization approach that encodes the parameters of the Gabor filters in a chromosome and uses genetic operators to optimize them. Filter selection is performed by grouping together filters having similar characteristics (i.e., similar parameters) using incremental clustering in the parameter space. Each group of filters is represented by a single filter whose parameters correspond to the average parameters of the filters in the group. This step eliminates redundant filters, leading to a compact, optimized set of filters. The average filters are evaluated using an application-oriented fitness criterion based on support vector machines (SVMs). To demonstrate the effectiveness of the proposed framework, we have considered the challenging problem of vehicle detection from gray-scale images. Our experimental results illustrate that the set of Gabor filters, specifically optimized for the problem of vehicle detection, yield better performance than using traditional filter banks.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Computational Intelligence IEEE Comput. Soc. Tech. Committee on Pattern Analysis and Machine Intelligence
Starting Page	307
Ending Page	314
File Size	500282
Page Count	8
File Format	PDF
ISBN	0769519784
DOI	10.1109/ICDM.2003.1250934
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gabor filters Vehicle detection Design optimization Pattern classification Algorithm design and analysis Genetic algorithms Biological cells Support vector machines Gray-scale Filter bank
Content Type	Text
Resource Type	Article

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