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Erschienen in:
Non-dominated Sorting Based Multi/Many-Objective Optimization: Two Decades of Research and Application Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

A Survey on the Application of Multi-Objective Optimization Methods in Image Segmentation

verfasst von : Niladri Sekhar Datta, Himadri Sekhar Dutta, Koushik Majumder, Sumana Chatterjee, Najir Abdul Wasim

Erschienen in: Multi-Objective Optimization

Verlag: Springer Singapore

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Abstract

A recent trend in problem formulation in image segmentation is to employ the multi-objective optimization (MOO) methods. The decision-making MOOs are the collection of realistic complex optimization problems, where the objective functions are usually conflicting. Image segmentation is the clustering of pixels applying definite criteria. It is one of the crucial parts in image processing. This chapter provides a comprehensive survey on MOO encompassing image segmentation problems. Here, the segmentation models are categorized by the problem formulation with a relevant optimization scheme. The survey also provides the latest direction and challenges of MOO in image segmentation procedure.

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Vorheriges Kapitel A Comparative Study on Different Versions of Multi-Objective Genetic Algorithm for Simultaneous Gene Selection and Sample Categorization
Nächstes Kapitel Bi-objective Genetic Algorithm with Rough Set Theory for Important Gene Selection in Disease Diagnosis
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Metadaten
Titel
A Survey on the Application of Multi-Objective Optimization Methods in Image Segmentation
verfasst von
Niladri Sekhar Datta
Himadri Sekhar Dutta
Koushik Majumder
Sumana Chatterjee
Najir Abdul Wasim
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Multi-Objective Optimization

Print ISBN: 978-981-13-1470-4

Electronic ISBN: 978-981-13-1471-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-13-1471-1_12

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