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2016 | OriginalPaper | Buchkapitel

Hybrid Swarms Optimization Based Image Segmentation

verfasst von : Mohamed Abd El Aziz, Ahmed A. Ewees, Aboul Ella Hassanien

Erschienen in: Hybrid Soft Computing for Image Segmentation

Verlag: Springer International Publishing

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Abstract

This chapter proposed multilevel thresholding hybrid swarms optimization algorithm for image segmentation. The proposed algorithm is inspired by the behavior of fireflies and real spider. It uses Firefly Algorithm (FA) and Social Spider Optimization (SSO) algorithm (FASSO). The objective function used for achieving multilevel thresholding is the maximum between class variance criterion. The proposed algorithm uses the FA to optimize threshold, and then uses this thresholding value to partition the images through SSO algorithm of a powerful global search capability. Experimental results demonstrate the effectiveness of the FASSO algorithm of image segmentation and provide faster convergence with relatively lower CPU time.

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Nächstes Kapitel Grayscale Image Segmentation Using Multilevel Thresholding and Nature-Inspired Algorithms
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Metadaten
Titel
Hybrid Swarms Optimization Based Image Segmentation
verfasst von
Mohamed Abd El Aziz
Ahmed A. Ewees
Aboul Ella Hassanien
Copyright-Jahr
2016
Buch
Hybrid Soft Computing for Image Segmentation

Print ISBN: 978-3-319-47222-5

Electronic ISBN: 978-3-319-47223-2

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-47223-2_1