Evaluations of Feature Extraction Programs Synthesized by Redundancy-removed Linear Genetic Programming: A Case Study on the Lawn Weed Detection Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{journals/jip/WatchareeruetaiTMKO10,

• author = "Ukrit Watchareeruetai and Yoshinori Takeuchi and Tetsuya Matsumoto and Hiroaki Kudo and Noboru Ohnishi",
• title = "Evaluations of Feature Extraction Programs Synthesized by Redundancy-removed Linear Genetic Programming: A Case Study on the Lawn Weed Detection Problem",
• journal = "Journal of Information Processing",
• year = "2010",
• volume = "18",
• pages = "164--174",
• month = apr,
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1882-6652",
• URL = "https://www.jstage.jst.go.jp/article/ipsjjip/18/0/18_0_164/_pdf/-char/en",
• DOI = "doi:10.2197/ipsjjip.18.164",
• size = "11 pages",
• abstract = "This paper presents an evolutionary synthesis of feature extraction programs for object recognition. The evolutionary synthesis method employed is based on linear genetic programming which is combined with redundancy-removed recombination. The evolutionary synthesis can automatically construct feature extraction programs for a given object recognition problem, without any domain-specific knowledge. Experiments were done on a lawn weed detection problem with both a low-level performance measure, i.e., segmentation accuracy, and an application-level performance measure, i.e., simulated weed control performance. Compared with four human-designed lawn weed detection methods, the results show that the performance of synthesised feature extraction programs is significantly better than three human-designed methods when evaluated with the low-level measure, and is better than two human-designed methods according to the application-level measure.",
• notes = "Department of Media Science, Graduate School of Information Science, Nagoya University",
• bibdate = "2011-09-14",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jip/jip18.html#WatchareeruetaiTMKO10",

}

Genetic Programming entries for Ukrit WatchAreeruetai Yoshinori Takeuchi Tetsuya Matsumoto Hiroaki Kudo Noboru Ohnishi

Citations