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Efficient Color Image Segmentation by a Parallel Optimized (ParaOptiMUSIG) Activation Function

Efficient Color Image Segmentation by a Parallel Optimized (ParaOptiMUSIG) Activation Function
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Author(s): Sourav De (University Institute of Technology, University of Burdwan, India), Siddhartha Bhattacharyya (RCC Institute of Information Technology, India) and Susanta Chakraborty (Bengal Engineering and Science University, India)
Copyright: 2014
Pages: 32
Source title: Global Trends in Intelligent Computing Research and Development
Source Author(s)/Editor(s): B.K. Tripathy (VIT University, India) and D. P. Acharjya (VIT University, India)
DOI: 10.4018/978-1-4666-4936-1.ch002

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Abstract

The optimized class responses from the image content has been applied to generate the optimized version of MUSIG (OptiMUSIG) activation function for a multilayer self organizing neural network architecture to effectively segment multilevel gray level intensity images. This chapter depicts the parallel version of the OptiMUSIG (ParaOptiMUSIG) activation function with the optimized class responses for the individual features with a parallel self-organizing neural network architecture to segment true color images. A genetic algorithm-based optimization technique has been employed to yield the optimized class responses in parallel. Comparison of the proposed method with the existing non-optimized method is applied on two real life true color images and is demonstrated with the help of three standard objective functions as they are employed to measure the quality of the segmented images. Results evolved by the ParaOptiMUSIG activation function are superior enough in comparison with the conventional nonoptimized MUSIG activation applied separately on the color gamut.

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