IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

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

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

Purchase

View Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSIG Activation Function on the publisher's website for pricing and purchasing information.

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.

Related Content

Paolo Massimo Buscema, William J. Tastle. © 2020. 29 pages.
Uthra Kunathur Thikshaja, Anand Paul. © 2020. 11 pages.
Arvind Kumar Tiwari. © 2020. 11 pages.
Srijan Das, Arpita Dutta, Saurav Sharma, Sangharatna Godboley. © 2020. 17 pages.
Mohammed E. El-Telbany, Samah Refat, Engy I. Nasr. © 2020. 13 pages.
Ashraf M. Abdelbar, Islam Elnabarawy, Donald C. Wunsch II, Khalid M. Salama. © 2020. 14 pages.
Saifullah Khalid. © 2020. 12 pages.
Body Bottom