GOOFeD: Extracting Advanced Features for Image Classification via Improved Genetic Programming
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gp-bibliography.bib Revision:1.8098
- @InProceedings{Price:2019:CEC,
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author = "Stanton R. Price and Derek T. Anderson and
Steven R. Price",
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title = "{GOOFeD:} Extracting Advanced Features for Image
Classification via Improved Genetic Programming",
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booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2019",
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pages = "1596--1603",
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abstract = "Feature extraction is widely considered one of the
most critical components to classification performance
in computer vision. In the past, human-designed
features, such as the histogram of oriented gradients,
were used for extracting statistically rich features.
Recently, there has been a movement away from
human-designed features to machine-learned features.
Herein, we propose a novel genetic programming (GP)
approach, coined GOOFeD, to automatically generate
discriminative-rich features for image classification.
This is achieved by greatly advancing GP in three ways:
(1) promoting population diversity and redundancy
removal, (2) introducing a unique adaptive mutation
approach, and (3) controlling tree bloat through a new
crossover technique. These improvements also lead to a
population size required for learning that is smaller
than that commonly used in the literature. To assess
performance, GOOFeD is tested on the MIT urban and
nature scene data set and a real-world buried explosive
hazard data set. Experiments verify that, in terms of
classification accuracy, GOOFeD outperforms many of the
state-of-the-art human-designed features and feature
learning techniques.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2019.8790347",
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month = jun,
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notes = "Also known as \cite{8790347}",
- }
Genetic Programming entries for
Stanton R Price
Derek T Anderson
Steven R Price
Citations