Genetic Programming Evolved Filters from a Small Number of Instances for Multiclass Texture Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{conf/ivcnz/Al-SahafZJ14,
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author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston",
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title = "Genetic Programming Evolved Filters from a Small
Number of Instances for Multiclass Texture
Classification",
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booktitle = "Proceedings of the 29th International Conference on
Image and Vision Computing New Zealand, {IVCNZ} 2014",
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publisher = "ACM",
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year = "2014",
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editor = "Michael J. Cree and Lee V. Streeter and
John Perrone and Michael Mayo and Anthony M. Blake",
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pages = "84--89",
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address = "Hamilton, New Zealand",
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month = nov # " 19-21",
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keywords = "genetic algorithms, genetic programming, Multiclass
classification, Textures",
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isbn13 = "978-1-4503-3184-5",
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bibdate = "2015-01-29",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ivcnz/ivcnz2014.html#Al-SahafZJ14",
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DOI = "doi:10.1145/2683405.2683418",
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acmid = "2683418",
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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
Citations