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Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function

Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function

Sourav De, Siddhartha Bhattacharyya, Susanta Chakraborty
Copyright: © 2016 |Pages: 28
ISBN13: 9781466694743|ISBN10: 1466694742|EISBN13: 9781466694750
DOI: 10.4018/978-1-4666-9474-3.ch011
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MLA

De, Sourav, et al. "Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function." Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications, edited by Siddhartha Bhattacharyya, et al., IGI Global, 2016, pp. 321-348. https://doi.org/10.4018/978-1-4666-9474-3.ch011

APA

De, S., Bhattacharyya, S., & Chakraborty, S. (2016). Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function. In S. Bhattacharyya, P. Banerjee, D. Majumdar, & P. Dutta (Eds.), Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications (pp. 321-348). IGI Global. https://doi.org/10.4018/978-1-4666-9474-3.ch011

Chicago

De, Sourav, Siddhartha Bhattacharyya, and Susanta Chakraborty. "Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function." In Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications, edited by Siddhartha Bhattacharyya, et al., 321-348. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9474-3.ch011

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Abstract

A self-supervised image segmentation method by a non-dominated sorting genetic algorithm-II (NSGA-II) based optimized MUSIG (OptiMUSIG) activation function with a multilayer self-organizing neural network (MLSONN) architecture is proposed to segment multilevel gray scale images. In the same way, another NSGA-II based parallel version of the OptiMUSIG (ParaOptiMUSIG) activation function with a parallel self-organizing neural network (PSONN) architecture is purported to segment the color images in this article. These methods are intended to overcome the drawback of their single objective based counterparts. Three standard objective functions are employed as the multiple objective criteria of the NSGA-II algorithm to measure the quality of the segmented images.

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