Presentation 1996/6/20
Image Retrieval Using Simulated Breeding by Pipeline-Type Genetic Algorithms
Asanobu KITAMOTO, Mikio TAKAGI,
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Abstract(in English) A method called simulated breeding is applied to the problem of learning criteria for similarity-based image retrieval. Usually traditional genetic algorithms without any modifications are used in the simulated breeding methods; however we propose a new model "pipeline-type genetic algorithms" that is more suitable for the simulated breeding methods, and also discuss genetic operators that are special to this model. We then show that this model can eliminate both the constraints and the restriction of population size, which are the disadvantage of the simulated breeding methods, and also demonstrate this model's powerful search capability and effective parallel processing. Finally we describe the application of this model to similarity-based image retrieval as a good man-machine interface.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) similarity-based image retrieval / genetic algorithms / simulated breeding / man-machine interface / parallel processing / image database
Paper # HIP96-4
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Committee HIP
Conference Date 1996/6/20(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Image Retrieval Using Simulated Breeding by Pipeline-Type Genetic Algorithms
Sub Title (in English)
Keyword(1) similarity-based image retrieval
Keyword(2) genetic algorithms
Keyword(3) simulated breeding
Keyword(4) man-machine interface
Keyword(5) parallel processing
Keyword(6) image database
1st Author's Name Asanobu KITAMOTO
1st Author's Affiliation Institute of Industrial Science, University of Tokyo()
2nd Author's Name Mikio TAKAGI
2nd Author's Affiliation Institute of Industrial Science, University of Tokyo
Date 1996/6/20
Paper # HIP96-4
Volume (vol) vol.96
Number (no) 115
Page pp.pp.-
#Pages 6
Date of Issue