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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
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Author(s): Sourav De (Cooch Behar Government Engineering College, India), Siddhartha Bhattacharyya (RCC Institute of Information Technology, India) and Susanta Chakraborty (Bengal Engineering & Science University, India)
Copyright: 2013
Pages: 41
Source title: Handbook of Research on Computational Intelligence for Engineering, Science, and Business
Source Author(s)/Editor(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India) and Paramartha Dutta (Visva-Bharati University, India)
DOI: 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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