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

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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Human Perception Based Color Image Segmentation

Computer Engineering and Applications Journal ◽

10.18495/comengapp.v2i3.34 ◽

2013 ◽

Vol 2 (3) ◽

Author(s):

Neeta Pradeep Gargote ◽

Savitha Devaraj ◽

Shravani Shahapure

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Network Architecture ◽

Color Image ◽

Color Space ◽

Human Perception ◽

Activation Function ◽

Color Image Segmentation ◽

Neural Network Architecture ◽

Hsi Color Space

Color image segmentation is probably the most important task in image analysis and understanding. A novel Human Perception Based Color Image Segmentation System is presented in this paper. This system uses a neural network architecture. The neurons here uses a multisigmoid activation function. The multisigmoid activation function is the key for segmentation. The number of steps ie. thresholds in the multisigmoid function are dependent on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSI color space. Here the main use of neural network is to detect the number of objects automatically from an image. It labels the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images.

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Color Image Segmentation of Seed Images Based on Self-Organizing Maps (SOM) Neural Network

Computación y Sistemas ◽

10.13053/cys-23-1-3141 ◽

2019 ◽

Vol 23 (1) ◽

Author(s):

J. M. Barrón Adame ◽

M. S. Acosta Navarrete ◽

J. Quintanilla Domínguez ◽

R. Guzmán Cabrera ◽

M. Cano Contreras ◽

...

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Color Image ◽

Color Image Segmentation ◽

Self Organizing Maps ◽

Som Neural Network ◽

Self Organizing

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

Global Trends in Intelligent Computing Research and Development - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-4936-1.ch002 ◽

2014 ◽

pp. 19-50 ◽

Cited By ~ 1

Author(s):

Sourav De ◽

Siddhartha Bhattacharyya ◽

Susanta Chakraborty

Keyword(s):

Neural Network ◽

Network Architecture ◽

Optimization Technique ◽

Activation Function ◽

Color Images ◽

Color Image Segmentation ◽

Neural Network Architecture ◽

Segmented Images ◽

True Color ◽

Self Organizing

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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