Genetic Programming Evolved Filters from a Small Number of Instances for Multiclass Texture Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/ivcnz/Al-SahafZJ14,

• author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston",
• title = "Genetic Programming Evolved Filters from a Small Number of Instances for Multiclass Texture Classification",
• booktitle = "Proceedings of the 29th International Conference on Image and Vision Computing New Zealand, {IVCNZ} 2014",
• publisher = "ACM",
• year = "2014",
• editor = "Michael J. Cree and Lee V. Streeter and John Perrone and Michael Mayo and Anthony M. Blake",
• pages = "84--89",
• address = "Hamilton, New Zealand",
• month = nov # " 19-21",
• keywords = "genetic algorithms, genetic programming, Multiclass classification, Textures",
• isbn13 = "978-1-4503-3184-5",
• bibdate = "2015-01-29",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ivcnz/ivcnz2014.html#Al-SahafZJ14",
• DOI = "doi:10.1145/2683405.2683418",
• acmid = "2683418",
• abstract = "Texture classification is an essential task in pattern recognition and computer vision. In this paper, a novel genetic programming (GP) based method is proposed for the task of multiclass texture classification. The proposed method evolves a set of filters using only two instances per class. Moreover, the evolved program operates directly on the raw pixel values and does not require human intervention to perform feature selection and extraction. Two well-known and widely used data sets are used in this study to evaluate the performance of the proposed method. The performance of the new method is compared to that of two GP-based methods using the raw pixel values, and six non-GP methods using three different sets of domain-specific features. The results show that the proposed method has significantly outperformed the other methods on both data sets.",
• URL = "http://dl.acm.org/citation.cfm?id=2683405",

}

Genetic Programming entries for Harith Al-Sahaf Mengjie Zhang Mark Johnston

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