Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation

Ranju Mandal; Basim Azam; Brijesh Verma; Mengjie Zhang

doi:10.1109/SSCI51031.2022.10022103

Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation

Ranju Mandal
, Basim Azam
, Brijesh Verma
, Mengjie Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Image segmentation is one of the major challenges in real-world computer vision applications. Context-embedded network models proposed for image segmentation have outperformed context-free models. However, optimized values of many parameters need to consider for such a complex network. The manual parameter selection process is ineffective and produces suboptimal performance for such a model. Therefore, we propose a context-based genetically optimized network model for image segmentation in this paper. Genetic algorithms enhance the performance of the deep network model by determining the best parameter values. The proposed three-level deep network is adaptable to image context by extracting visual and context-rich features and optimally integrating them to obtain final pixel labels for scene images. The genetic algorithm ensures optimal parameter values in all three levels to obtain a globally optimized network model to achieve the best segmentation results.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Editors	Hisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	642-648
Number of pages	7
ISBN (Electronic)	9781665487689
DOIs	https://doi.org/10.1109/SSCI51031.2022.10022103
Publication status	Published - 2022
Externally published	Yes
Event	2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 - Singapore, Singapore Duration: 4 Dec 2022 → 7 Dec 2022

Publication series

Name	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

Conference

Conference	2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Country/Territory	Singapore
City	Singapore
Period	4/12/22 → 7/12/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 14 Life Below Water

Keywords

Deep Learning
Genetic Algorithm
Image Segmentation
Scene Parsing
Semantic Segmentation

Access to Document

10.1109/SSCI51031.2022.10022103

Cite this

Mandal, R., Azam, B., Verma, B., & Zhang, M. (2022). Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation. In H. Ishibuchi, C.-K. Kwoh, A.-H. Tan, D. Srinivasan, C. Miao, A. Trivedi, & K. Crockett (Eds.), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 642-648). (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI51031.2022.10022103

Mandal, Ranju ; Azam, Basim ; Verma, Brijesh et al. / Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation. Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. editor / Hisao Ishibuchi ; Chee-Keong Kwoh ; Ah-Hwee Tan ; Dipti Srinivasan ; Chunyan Miao ; Anupam Trivedi ; Keeley Crockett. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 642-648 (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022).

@inproceedings{2acbe3e7a2154b6c912141b902d3688d,

title = "Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation",

abstract = "Image segmentation is one of the major challenges in real-world computer vision applications. Context-embedded network models proposed for image segmentation have outperformed context-free models. However, optimized values of many parameters need to consider for such a complex network. The manual parameter selection process is ineffective and produces suboptimal performance for such a model. Therefore, we propose a context-based genetically optimized network model for image segmentation in this paper. Genetic algorithms enhance the performance of the deep network model by determining the best parameter values. The proposed three-level deep network is adaptable to image context by extracting visual and context-rich features and optimally integrating them to obtain final pixel labels for scene images. The genetic algorithm ensures optimal parameter values in all three levels to obtain a globally optimized network model to achieve the best segmentation results.",

keywords = "Deep Learning, Genetic Algorithm, Image Segmentation, Scene Parsing, Semantic Segmentation",

author = "Ranju Mandal and Basim Azam and Brijesh Verma and Mengjie Zhang",

note = "Funding Information: AC KFNOWDLGE0D\#GM:EN0T ! T!his research work is supported by\% the Australian R{"}esearch Council's9 D\#iscov\&ery\% PEroje*ct ((\#DPE220C0C1.0C2225226)2 ) funding scheme. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 ; Conference date: 04-12-2022 Through 07-12-2022",

year = "2022",

doi = "10.1109/SSCI51031.2022.10022103",

language = "English",

series = "Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "642--648",

editor = "Hisao Ishibuchi and Chee-Keong Kwoh and Ah-Hwee Tan and Dipti Srinivasan and Chunyan Miao and Anupam Trivedi and Keeley Crockett",

booktitle = "Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022",

}

Mandal, R, Azam, B, Verma, B & Zhang, M 2022, Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation. in H Ishibuchi, C-K Kwoh, A-H Tan, D Srinivasan, C Miao, A Trivedi & K Crockett (eds), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Institute of Electrical and Electronics Engineers Inc., pp. 642-648, 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Singapore, Singapore, 4/12/22. https://doi.org/10.1109/SSCI51031.2022.10022103

Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation. / Mandal, Ranju; Azam, Basim; Verma, Brijesh et al.
Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. ed. / Hisao Ishibuchi; Chee-Keong Kwoh; Ah-Hwee Tan; Dipti Srinivasan; Chunyan Miao; Anupam Trivedi; Keeley Crockett. Institute of Electrical and Electronics Engineers Inc., 2022. p. 642-648 (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation

AU - Mandal, Ranju

AU - Azam, Basim

AU - Verma, Brijesh

AU - Zhang, Mengjie

N1 - Funding Information: AC KFNOWDLGE0D#GM:EN0T ! T!his research work is supported by% the Australian R"esearch Council's9 D#iscov&ery% PEroje*ct ((#DPE220C0C1.0C2225226)2 ) funding scheme. Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - Image segmentation is one of the major challenges in real-world computer vision applications. Context-embedded network models proposed for image segmentation have outperformed context-free models. However, optimized values of many parameters need to consider for such a complex network. The manual parameter selection process is ineffective and produces suboptimal performance for such a model. Therefore, we propose a context-based genetically optimized network model for image segmentation in this paper. Genetic algorithms enhance the performance of the deep network model by determining the best parameter values. The proposed three-level deep network is adaptable to image context by extracting visual and context-rich features and optimally integrating them to obtain final pixel labels for scene images. The genetic algorithm ensures optimal parameter values in all three levels to obtain a globally optimized network model to achieve the best segmentation results.

AB - Image segmentation is one of the major challenges in real-world computer vision applications. Context-embedded network models proposed for image segmentation have outperformed context-free models. However, optimized values of many parameters need to consider for such a complex network. The manual parameter selection process is ineffective and produces suboptimal performance for such a model. Therefore, we propose a context-based genetically optimized network model for image segmentation in this paper. Genetic algorithms enhance the performance of the deep network model by determining the best parameter values. The proposed three-level deep network is adaptable to image context by extracting visual and context-rich features and optimally integrating them to obtain final pixel labels for scene images. The genetic algorithm ensures optimal parameter values in all three levels to obtain a globally optimized network model to achieve the best segmentation results.

KW - Deep Learning

KW - Genetic Algorithm

KW - Image Segmentation

KW - Scene Parsing

KW - Semantic Segmentation

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DO - 10.1109/SSCI51031.2022.10022103

M3 - Conference contribution

AN - SCOPUS:85147798694

T3 - Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

SP - 642

EP - 648

BT - Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

A2 - Ishibuchi, Hisao

A2 - Kwoh, Chee-Keong

A2 - Tan, Ah-Hwee

A2 - Srinivasan, Dipti

A2 - Miao, Chunyan

A2 - Trivedi, Anupam

A2 - Crockett, Keeley

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

Y2 - 4 December 2022 through 7 December 2022

ER -

Mandal R, Azam B, Verma B, Zhang M. Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation. In Ishibuchi H, Kwoh CK, Tan AH, Srinivasan D, Miao C, Trivedi A, Crockett K, editors, Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 642-648. (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022). doi: 10.1109/SSCI51031.2022.10022103

Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation

Abstract

Publication series

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UN SDGs

Keywords

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