A honey-bee-mating based algorithm for multilevel image segmentation using Bayesian theorem

Jiang, Y; Tsai, P; Yeh, WC; Cao, L

A honey-bee-mating based algorithm for multilevel image segmentation using Bayesian theorem

Jiang, Y Tsai, P Yeh, WC Cao, L

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Applied Soft Computing, 2017, 52, pp. 1181-1190
Issue Date:: 2017-03-01

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Field	Value	Language
dc.contributor.author	Jiang, Y
dc.contributor.author	Tsai, P
dc.contributor.author	Yeh, WC
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429
dc.date.accessioned	2022-07-12T01:08:09Z
dc.date.available	2022-07-12T01:08:09Z
dc.date.issued	2017-03-01
dc.identifier.citation	Applied Soft Computing, 2017, 52, pp. 1181-1190
dc.identifier.issn	1568-4946
dc.identifier.issn	1872-9681
dc.identifier.uri	http://hdl.handle.net/10453/158790
dc.description.abstract	The image thresholding techniques are considered as a must for objects segmentation, compression and target recognition, and they have been widely studied for the last few decades; for example, the multi-level thresholding methods, and as such (they) render more great challenges for image segmentation techniques that remain computationally more expensive, when their choices of threshold numbers were increased. Therefore, our aim was to propose an algorithm based on Bayesian theorem and the so-called honey-bee-mating algorithm (HBMA), called a Bayesian honey bee mating algorithm BHBMA. It can not only reduce the computational time and curse of dimensionality, but also can run more reliably and more stably. This enhanced capability was technically accomplished by embedding a new population initialization strategy based on the characteristics of multi-level thresholding technique in pixel-based intensity images arranged from lower grey levels to higher ones. Extensive experiments have shown that our proposed method outperformed other state-of-the-art algorithms empirically in terms of their effectiveness and efficiency, when applying to complex image processing scenario such as automatic target recognition.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Applied Soft Computing
dc.relation.isbasedon	10.1016/j.asoc.2016.09.008
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A honey-bee-mating based algorithm for multilevel image segmentation using Bayesian theorem
dc.type	Journal Article
utslib.citation.volume	52
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-07-12T01:08:08Z
pubs.publication-status	Published
pubs.volume	52

Abstract:

The image thresholding techniques are considered as a must for objects segmentation, compression and target recognition, and they have been widely studied for the last few decades; for example, the multi-level thresholding methods, and as such (they) render more great challenges for image segmentation techniques that remain computationally more expensive, when their choices of threshold numbers were increased. Therefore, our aim was to propose an algorithm based on Bayesian theorem and the so-called honey-bee-mating algorithm (HBMA), called a Bayesian honey bee mating algorithm BHBMA. It can not only reduce the computational time and curse of dimensionality, but also can run more reliably and more stably. This enhanced capability was technically accomplished by embedding a new population initialization strategy based on the characteristics of multi-level thresholding technique in pixel-based intensity images arranged from lower grey levels to higher ones. Extensive experiments have shown that our proposed method outperformed other state-of-the-art algorithms empirically in terms of their effectiveness and efficiency, when applying to complex image processing scenario such as automatic target recognition.

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http://hdl.handle.net/10453/158790