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Application of evolutionary and swarm optimization in computer vision: a literature survey

Nakane, Takumi, Bold, Naranchimeg, Sun, Haitian, Lu, Xuequan, Akashi, Takuya and Zhang, Chao 2020, Application of evolutionary and swarm optimization in computer vision: a literature survey, IPSJ Transactions on Computer Vision and Applications, vol. 12, no. 3, pp. 1-34, doi: 10.1186/s41074-020-00065-9.

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Title Application of evolutionary and swarm optimization in computer vision: a literature survey
Author(s) Nakane, Takumi
Bold, Naranchimeg
Sun, Haitian
Lu, XuequanORCID iD for Lu, Xuequan orcid.org/0000-0003-0959-408X
Akashi, Takuya
Zhang, Chao
Journal name IPSJ Transactions on Computer Vision and Applications
Volume number 12
Issue number 3
Start page 1
End page 34
Total pages 34
Publisher SpringerOpen
Place of publication London, Eng.
Publication date 2020
ISSN 1882-6695
1882-6695
Keyword(s) Evolutionary algorithms
Swarm algorithms
Computer vision
Literature survey
Summary Evolutionary algorithms (EAs) and swarm algorithms (SAs) have shown their usefulness in solving combinatorial and NP-hard optimization problems in various research fields. However, in the field of computer vision, related surveys have not been updated during the last decade. In this study, inspired by the recent development of deep neural networks in computer vision, which embed large-scale optimization problems, we first describe a literature survey conducted to compensate for the lack of relevant research in this area. Specifically, applications related to the genetic algorithm and differential evolution from EAs, as well as particle swarm optimization and ant colony optimization from SAs and their variants, are mainly considered in this survey.
Language eng
DOI 10.1186/s41074-020-00065-9
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30141575

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.