Genetic Programming with Aggregate Channel Features for Flower Localization Using Limited Training Data

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

@InProceedings{Wang:2024:evoapplications,

• author = "Qinyu Wang and Ying Bi and Bing Xue and Mengjie Zhang",
• title = "Genetic Programming with Aggregate Channel Features for Flower Localization Using Limited Training Data",
• booktitle = "27th International Conference, EvoApplications 2024",
• year = "2024",
• editor = "Stephen Smith and Joao Correia and Christian Cintrano",
• series = "LNCS",
• volume = "14635",
• publisher = "Springer",
• address = "Aberystwyth",
• month = "3-5 " # apr,
• pages = "196--211",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Aggregate channel features, Flower localisation",
• isbn13 = "978-3-031-56854-1",
• URL = "https://rdcu.be/dD0js",
• DOI = "doi:10.1007/978-3-031-56855-8_12",
• size = "16 pages",
• abstract = "Flower localisation is a crucial image pre-processing step for subsequent classification/recognition that confronts challenges with diverse flower species, varying imaging conditions, and limited data. Existing flower localisation methods face limitations, including reliance on colour information, low model interpretability, and a large demand for training data. This paper proposes a new genetic programming (GP) approach called ACFGP with a novel representation to automated flower localisation with limited training data. The novel GP representation enables ACFGP to evolve effective programs for generating aggregate channel features and achieving flower localization in diverse scenarios. Comparative evaluations against the baseline benchmark algorithm and YOLOv8 demonstrate ACFGP superior performance. Further analysis highlights the effectiveness of the aggregate channel features generated by ACFGP programs, demonstrating the superiority of ACFGP in addressing challenging flower localisation tasks.",
• notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024",

}

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

Citations