Automatically Evolving Texture Image Descriptors using the Multi-tree Representation in Genetic Programming using Few Instances
Created by W.Langdon from
gp-bibliography.bib Revision:1.5787
- @Article{Al-Sahaf:EC,
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author = "Harith Al-Sahaf and Ausama Al-Sahaf and Bing Xue and
Mengjie Zhang",
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title = "Automatically Evolving Texture Image Descriptors using
the Multi-tree Representation in Genetic Programming
using Few Instances",
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journal = "Evolutionary Computation",
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note = "Forthcoming",
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keywords = "genetic algorithms, genetic programming, ANN, image
descriptor, multi-tree, image classification, feature
extraction",
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ISSN = "1063-6560",
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URL = "
https://doi.org/10.1162/evco_a_00284",
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DOI = "
doi:10.1162/evco_a_00284",
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size = "34 pages",
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abstract = "The performance of image classification is highly
dependent on the quality of the extracted features that
are used to build a model. Designing such features
usually requires prior knowledge of the domain and is
often undertaken by a domain expert who, if available,
is very costly to employ. Automating the process of
designing such features can largely reduce the cost and
efforts associated with this task. Image descriptors,
such as local binary patterns, have emerged in computer
vision, and aim at detecting keypoints, e.g., corners,
line-segments and shapes, in an image and extracting
features from those key points. In this paper, genetic
programming (GP) is used to automatically evolve an
image descriptor using only two instances per class by
using a multi-tree program representation. The
automatically evolved descriptor operates directly on
the raw pixel values of an image and generates the
corresponding feature vector. Seven well-known datasets
were adapted to the f",
- }
Genetic Programming entries for
Harith Al-Sahaf
Ausama Al-Sahaf
Bing Xue
Mengjie Zhang
Citations