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2017 | OriginalPaper | Chapter

2. Multilevel Segmentation in Digital Images

Authors : Erik Cuevas, Valentín Osuna, Diego Oliva

Published in: Evolutionary Computation Techniques: A Comparative Perspective

Publisher: Springer International Publishing

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Abstract

Segmentation is used to divide an image into separate regions, which in fact correspond to different real-world objects. One interesting functional criterion for segmentation is the Tsallis entropy (TE), which gives excellent results in bi-level thresholding. However, when it is applied to multilevel thresholding (MT), its evaluation becomes computationally expensive, since each threshold point adds restrictions, multimodality and complexity to its functional formulation. In this chapter, a new algorithm for multilevel segmentation based on the Electromagnetism-Like algorithm (EMO) is presented. In the approach, the EMO algorithm is used to find the optimal threshold values by maximizing the Tsallis entropy. Experimental results over several images demonstrate that the proposed approach is able to improve the convergence velocity, compared with similar methods such as Cuckoo search, and Particle Swarm Optimization.

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Metadata
Title
Multilevel Segmentation in Digital Images
Authors
Erik Cuevas
Valentín Osuna
Diego Oliva
Copyright Year
2017
Book
Evolutionary Computation Techniques: A Comparative Perspective

Print ISBN: 978-3-319-51108-5

Electronic ISBN: 978-3-319-51109-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-51109-2_2

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