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2013 | OriginalPaper | Buchkapitel

3. Hybrid BBO-DE Algorithms for Fuzzy Entropy-Based Thresholding

verfasst von : Ilhem Boussaïd, Amitava Chatterjee, Patrick Siarry, Mohamed Ahmed-Nacer

Erschienen in: Computational Intelligence in Image Processing

Verlag: Springer Berlin Heidelberg

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Abstract

This chapter shows how a recently proposed stochastic optimization algorithm, called biogeography-based optimization (BBO), can be efficiently employed for development of three-level thresholding-based image segmentation. This technique is utilized to determine suitable thresholds utilizing a fuzzy entropy-based fitness function, which the optimization procedure attempts to maximize. The chapter demonstrates how improved BBO-based strategies, employing hybridizations with differential evolution (DE) algorithms, can be employed to incorporate diversity in the basic BBO algorithm that can help the optimization algorithm avoid getting trapped at local optima and seek the global optimum in a more efficient manner. Several such hybrid BBO-DE algorithms have been utilized for this optimum thresholding-based image segmentation procedure. A detailed implementation analysis for a popular set of well-known benchmark images has been carried out to qualitatively and quantitatively demonstrate the utility of the proposed hybrid BBO-DE optimization algorithm.

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Metadaten
Titel
Hybrid BBO-DE Algorithms for Fuzzy Entropy-Based Thresholding
verfasst von
Ilhem Boussaïd
Amitava Chatterjee
Patrick Siarry
Mohamed Ahmed-Nacer
Copyright-Jahr
2013
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-30621-1_3