Genetic Programming With Flexible Region Detection for Fine-Grained Image Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{Wang:TEVC,
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author = "Qinyu Wang and Ying Bi and Bing Xue and
Mengjie Zhang",
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journal = "IEEE Transactions on Evolutionary Computation",
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title = "Genetic Programming With Flexible Region Detection for
Fine-Grained Image Classification",
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note = "Early access",
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abstract = "Fine-grained image classification (FGIC) is an
important computer vision task with many real-world
applications. However, FGIC is challenging due to
intra-class variations and inter-class similarities,
especially when there is limited training data. To
address these challenges, a new genetic programming
approach with flexible region detection, GP-RD, is
proposed for different FGIC tasks, i.e., flower and
fish classification tasks. The proposed GP-RD approach
can automatically highlight the object, detect regions
of interest, extract effective features, and combine
global, local, and/or colour features for
classification. The performance of GP-RD is evaluated
on flower and fish classification tasks within the FGIC
domain, using datasets with varying classes. In
comparison with seven benchmark methods, GP-RD achieves
significantly better performance in most comparisons.
Further analysis demonstrates the interpretability,
effectiveness, and efficiency of the proposed
approach.",
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keywords = "genetic algorithms, genetic programming, Feature
extraction, Task analysis, Image classification,
Training, Flowering plants, Fish, Training data, Region
Detection, Fine-Grained Image Classification, Feature
Extraction",
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DOI = "doi:10.1109/TEVC.2024.3379257",
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ISSN = "1941-0026",
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notes = "Also known as \cite{10475668}",
- }
Genetic Programming entries for
Qinyu Wang
Ying Bi
Bing Xue
Mengjie Zhang
Citations