Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Al-Sahaf:2017a:ieeeTEC,
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author = "Harith Al-Sahaf and Ausama Al-Sahaf and Bing Xue and
Mark Johnston and Mengjie Zhang",
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title = "Automatically Evolving Rotation-Invariant Texture
Image Descriptors by Genetic Programming",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2017",
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volume = "21",
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number = "1",
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pages = "83--101",
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month = feb,
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keywords = "genetic algorithms, genetic programming,
Classification, feature extraction, image descriptor,
keypoint detection",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2016.2577548",
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size = "19 pages",
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abstract = "In computer vision, training a model that performs
classification effectively is highly dependent on the
extracted features, and the number of training
instances. Conventionally, feature detection and
extraction are performed by a domain expert who, in
many cases, is expensive to employ and hard to find.
Therefore, image descriptors have emerged to automate
these tasks. However, designing an image descriptor
still requires domain-expert intervention. Moreover,
the majority of machine learning algorithms require a
large number of training examples to perform well.
However, labelled data is not always available or easy
to acquire, and dealing with a large dataset can
dramatically slow down the training process. In this
paper, we propose a novel genetic programming-based
method that automatically synthesises a descriptor
using only two training instances per class. The
proposed method combines arithmetic operators to evolve
a model that takes an image and generates a feature
vector. The performance of the proposed method is
assessed using six datasets for texture classification
with different degrees of rotation and is compared with
seven domain-expert designed descriptors. The results
show that the proposed method is robust to rotation and
has significantly outperformed, or achieved a
comparable performance to, the baseline methods.",
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notes = "also known as \cite{7486119}",
- }
Genetic Programming entries for
Harith Al-Sahaf
Ausama Al-Sahaf
Bing Xue
Mark Johnston
Mengjie Zhang
Citations