Genetic Programming with Aggregate Channel Features for Flower Localization Using Limited Training Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Wang:2024:evoapplications,
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author = "Qinyu Wang and Ying Bi and Bing Xue and
Mengjie Zhang",
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title = "Genetic Programming with Aggregate Channel Features
for Flower Localization Using Limited Training Data",
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booktitle = "27th International Conference, EvoApplications 2024",
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year = "2024",
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editor = "Stephen Smith and Joao Correia and
Christian Cintrano",
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series = "LNCS",
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volume = "14635",
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publisher = "Springer",
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address = "Aberystwyth",
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month = "3-5 " # apr,
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pages = "196--211",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Aggregate
channel features, Flower localisation",
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isbn13 = "978-3-031-56854-1",
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URL = "https://rdcu.be/dD0js",
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DOI = "doi:10.1007/978-3-031-56855-8_12",
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size = "16 pages",
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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.",
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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