NDLI: Transformation of redundant representations of linear genetic programming into canonical forms for efficient extraction of image features

Content Provider	IEEE Xplore Digital Library
Author	Watchareeruetai, U. Takeuchi, Y. Matsumoto, T. Kudo, H. Ohnishi, N.
Copyright Year	2008
Description	Author affiliation: Dept. of Media Sci., Nagoya Univ., Nagoya (Watchareeruetai, U.; Takeuchi, Y.; Matsumoto, T.; Kudo, H.; Ohnishi, N.)
Abstract	Recently, evolutionary computation (EC) has been adopted to search for effective feature extraction programs for given image recognition problems. For this approach, feature extraction programs are constructed from a set of primitive operations (POs), which are usually general image processing and pattern recognition operations. In this paper, we focus on an approach based on a variation of linear genetic programming (LGP). We describe the causes of redundancies in LGP based representation, and propose a transformation that converts the redundant LGP representation into a canonical form, in which all redundancies are removed. Based on this transformation, we present a way to reduce computation time, i.e., the evolutionary search that avoids executions of redundant individuals. Experimental results demonstrate a success in computation time reduction; around 7-62% of total computation time can be reduced. Also, we have experimented with an evolutionary search that prohibits existence of redundant individuals. When selection pressure is high enough, its search performance is better than that of conventional evolutionary search.
Starting Page	1996
Ending Page	2003
File Size	337615
Page Count	8
File Format	PDF
ISBN	9781424418220
DOI	10.1109/CEC.2008.4631062
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Registers Feature extraction Redundancy Evolutionary computation Indexes Arrays Training
Content Type	Text
Resource Type	Article

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