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Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSIG Activation Function

Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSIG Activation Function

Sourav De, Siddhartha Bhattacharyya, Susanta Chakraborty
Copyright: © 2013 |Pages: 41
ISBN13: 9781466625181|ISBN10: 146662518X|EISBN13: 9781466625198
DOI: 10.4018/978-1-4666-2518-1.ch005
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MLA

De, Sourav, et al. "Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSIG Activation Function." Handbook of Research on Computational Intelligence for Engineering, Science, and Business, edited by Siddhartha Bhattacharyya and Paramartha Dutta, IGI Global, 2013, pp. 122-162. https://doi.org/10.4018/978-1-4666-2518-1.ch005

APA

De, S., Bhattacharyya, S., & Chakraborty, S. (2013). Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSIG Activation Function. In S. Bhattacharyya & P. Dutta (Eds.), Handbook of Research on Computational Intelligence for Engineering, Science, and Business (pp. 122-162). IGI Global. https://doi.org/10.4018/978-1-4666-2518-1.ch005

Chicago

De, Sourav, Siddhartha Bhattacharyya, and Susanta Chakraborty. "Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSIG Activation Function." In Handbook of Research on Computational Intelligence for Engineering, Science, and Business, edited by Siddhartha Bhattacharyya and Paramartha Dutta, 122-162. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2518-1.ch005

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Abstract

The proposed chapter is intended to propose a self supervised image segmentation method by a multi-objective genetic algorithm based optimized MUSIG (OptiMUSIG) activation function with a multilayer self organizing neural network architecture to segment multilevel gray scale intensity images. The multiobjective genetic algorithm based parallel version of the OptiMUSIG (ParaOptiMUSIG) activation function with a parallel self organizing neural network architecture is also discussed to segment true color images. These methods are quite efficient enough to overcome the drawbacks of the single objective based OptiMUSIG and ParaOptiMUSIG activation functions to segment gray scale and true color images, respectively. The proposed multiobjective genetic algorithm based optimization methods are applied on three standard objective functions to measure the quality of the segmented images. These functions form the multiple objective criteria of the multiobjective genetic algorithm based image segmentation method.

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