Towards Genetic Programming for Texture Classification

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

@InProceedings{Song:2001:TGP,

• author = "Andy Song and Thomas Loveard and Vic Ciesielski",
• title = "Towards Genetic Programming for Texture Classification",
• booktitle = "Proceedings of the 14th International Joint Conference on Artificial Intelligence AI 2001: Advances in Artificial Intelligence",
• volume = "2256",
• pages = "461--472",
• year = "2001",
• editor = "M. Stumptner and D. Corbett and M. Brooks",
• series = "Lecture Notes in Computer Science",
• address = "Adelaide, Australia",
• publisher_address = "Heidelberg",
• month = dec # " 10-14",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-540-42960-9",
• CODEN = "LNCSD9",
• ISSN = "0302-9743",
• bibdate = "Fri Mar 8 07:56:44 MST 2002",
• DOI = "doi:10.1007/3-540-45656-2_40",
• acknowledgement = ack-nhfb,
• abstract = "The genetic programming (GP) method is proposed as a new approach to perform texture classification based directly on raw pixel data. Two alternative genetic programming representations are used to perform classification. These are dynamic range selection (DRS) and static range selection (SRS). This preliminary study uses four brodatz textures to investigate the applicability of the genetic programming method for binary texture classifications and multi-texture classifications. Results indicate that the genetic programming method, based directly on raw pixel data, is able to accurately classify different textures. The results show that the DRS method is well suited to the task of texture classification. The classifiers generated in our experiments by DRS have good performance over a variety of texture data and offer GP as a promising alternative approach for the difficult problem of texture classification.",

}

Genetic Programming entries for Andy Song Thomas Loveard Victor Ciesielski

Citations