Image Descriptor: A Genetic Programming Approach to Multiclass Texture Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Al-Sahaf:2015:CEC,
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author = "Harith Al-Sahaf and Mengjie Zhang and
Mark Johnston and Brijesh Verma",
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title = "Image Descriptor: A Genetic Programming Approach to
Multiclass Texture Classification",
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booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary
Computation (CEC 2015)",
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year = "2015",
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editor = "Yadahiko Murata",
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pages = "2460--2467",
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address = "Sendai, Japan",
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month = "25-28 " # may,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4799-7491-7",
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DOI = "doi:10.1109/CEC.2015.7257190",
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size = "8 pages",
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abstract = "Texture classification is an essential task in
computer vision that aims at grouping instances that
have a similar repetitive pattern into one group.
Detecting texture primitives can be used to
discriminate between materials of different types. The
process of detecting prominent features from the
texture instances represents a cornerstone step in
texture classification. Moreover, building a good model
using a few training instances is difficult. In this
study, a genetic programming (GP) descriptor is
proposed for the task of multiclass texture
classification. The proposed method synthesises a set
of mathematical formulas relying on the raw pixel
values and a sliding window of a predetermined size.
Furthermore, only two instances per class are used to
automatically evolve a descriptor that has the
potential to effectively discriminate between instances
of different textures using a simple instance-based
classifier to perform the classification task. The
performance of the proposed approach is examined using
two widely-used data sets, and compared with two
GP-based and nine well-known non-GP methods.
Furthermore, three hand-crafted domain-expert designed
feature extraction methods have been used with the
non-GP methods to examine the effectiveness of the
proposed method. The results show that the proposed
method has significantly outperformed all these other
methods on both data sets, and the new method evolves a
descriptor that is capable of achieving significantly
better performance compared to hand-crafted features.",
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notes = "1340 hrs 15390 CEC2015",
- }
Genetic Programming entries for
Harith Al-Sahaf
Mengjie Zhang
Mark Johnston
Brijesh Verma
Citations