A One-Shot Learning Approach to Image Classification Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Al-Sahaf:2013:AI,
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author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston",
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title = "A One-Shot Learning Approach to Image Classification
Using Genetic Programming",
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booktitle = "Proceedings of the 26th Australasian Joint Conference
on Artificial Intelligence (AI2013)",
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year = "2013",
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editor = "Stephen Cranefield and Abhaya Nayak",
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volume = "8272",
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series = "LNAI",
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pages = "110--122",
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address = "Dunedin, New Zealand",
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month = "1-6 " # dec,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Local Binary
Patterns, Image Classification, One-shot Learning",
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isbn13 = "978-3-319-03679-3",
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URL = "http://dx.doi.org/10.1007/978-3-319-03680-9_13",
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DOI = "doi:10.1007/978-3-319-03680-9_13",
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size = "13 pages",
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abstract = "In machine learning, it is common to require a large
number of instances to train a model for
classification. In many cases, it is hard or expensive
to acquire a large number of instances. In this paper,
we propose a novel genetic programming (GP) based
method to the problem of automatic image classification
via adopting a one-shot learning approach. The proposed
method relies on the combination of GP and Local Binary
Patterns (LBP) techniques to detect a predefined number
of informative regions that aim at maximising the
between-class scatter and minimising the within-class
scatter. Moreover, the proposed method uses only two
instances of each class to evolve a classifier. To test
the effectiveness of the proposed method, four
different texture data sets are used and the
performance is compared against two other GP-based
methods namely Conventional GP and Two-tier GP. The
experiments revealed that the proposed method
outperforms these two methods on all the data sets.
Moreover, a better performance has been achieved by
Naive Bayes, Support Vector Machine, and Decision Trees
(J48) methods when extracted features by the proposed
method have been used compared to the use of
domain-specific and Two-tier GP extracted features.",
- }
Genetic Programming entries for
Harith Al-Sahaf
Mengjie Zhang
Mark Johnston
Citations