Genetic Programming With Flexible Region Detection for Fine-Grained Image Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Wang:TEVC,

• author = "Qinyu Wang and Ying Bi and Bing Xue and Mengjie Zhang",
• journal = "IEEE Transactions on Evolutionary Computation",
• title = "Genetic Programming With Flexible Region Detection for Fine-Grained Image Classification",
• note = "Early access",
• 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.",
• 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",
• DOI = "doi:10.1109/TEVC.2024.3379257",
• ISSN = "1941-0026",
• notes = "Also known as \cite{10475668}",

}

Genetic Programming entries for Qinyu Wang Ying Bi Bing Xue Mengjie Zhang

Citations