Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function
- 1 January 2016
- book chapter
- other
- Published by IGI Global
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.This publication has 27 references indexed in Scilit:
- A parallel bi-directional self-organizing neural network (PBDSONN) architecture for color image extraction and segmentationNeurocomputing, 2012
- Self-Organizing Map-Based Color Image Segmentation with k-Means Clustering and Saliency MapISRN Signal Processing, 2011
- Multilevel image segmentation with adaptive image context based thresholdingApplied Soft Computing, 2011
- A Brief Survey of Color Image Preprocessing and Segmentation TechniquesJournal of Pattern Recognition Research, 2011
- Local adaptive receptive field self-organizing map for image color segmentationImage and Vision Computing, 2009
- Automatic Clustering Using an Improved Differential Evolution AlgorithmIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2007
- Multicomponent Image Segmentation Using a Genetic Algorithm and Artificial Neural NetworkIEEE Geoscience and Remote Sensing Letters, 2007
- Multiobjective Genetic Clustering for Pixel Classification in Remote Sensing ImageryIEEE Transactions on Geoscience and Remote Sensing, 2007
- Quantitative evaluation of color image segmentation resultsPattern Recognition Letters, 1998
- A Cluster Separation MeasureIEEE Transactions on Pattern Analysis and Machine Intelligence, 1979