Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Al-Sahaf:2015:EC,
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author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston",
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title = "Binary Image Classification: A Genetic Programming
Approach to the Problem of Limited Training Instances",
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journal = "Evolutionary Computation",
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year = "2016",
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volume = "24",
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number = "1",
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pages = "143--182",
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month = "Spring",
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keywords = "genetic algorithms, genetic programming, Local Binary
Patterns, One-shot Learning, Image Classification",
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ISSN = "1063-6560",
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DOI = "doi:10.1162/EVCO_a_00146",
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size = "37 pages",
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abstract = "In the Computer Vision and Pattern Recognition fields,
image classification represents an important, yet
difficult, task to perform. The remarkable ability of
the human visual system, which relies on only one or a
few instances to learn a completely new class or an
object of a class, is a challenge to build effective
computer models to replicate this ability. Recently, we
have proposed two Genetic Programming (GP) based
methods, One-shot GP and Compound-GP, that aim to
evolve a program for the task of binary classification
in images. The two methods are designed to use only one
or a few instances per class to evolve the model. In
this study, we investigate these two methods in terms
of performance, robustness, and complexity of the
evolved programs. Ten data sets that vary in difficulty
have been used to evaluate these two methods. We also
compare them with two other GP and six non-GP methods.
The results show that One-shot GP and Compound-GP
outperform or achieve comparable results to other
competitor methods. Moreover, the features extracted by
these two methods improve the performance of other
classifiers with handcrafted features and those
extracted by a recently developed GP-based method in
most cases",
- }
Genetic Programming entries for
Harith Al-Sahaf
Mengjie Zhang
Mark Johnston
Citations