A multilevel sigmoidal (MUSIG) activation function is efficient in segmenting multilevel images. ... more A multilevel sigmoidal (MUSIG) activation function is efficient in segmenting multilevel images. The function uses equal and fixed class responses, assuming the homogeneity of image information content. In this article, a novel approach for generating optimized class responses of the MUSIG activation function, is proposed. Three different types of objective function are used to measure the quality of the segmentation in the proposed genetic algorithm based optimization method. Results of segmentation of two real life images by the optimized MUSIG (OptiMUSIG) activation function with optimized class responses show better performances over the MUSIG activation function with equal and fixed responses.
A novel neuro-fuzzy-genetic approach is presented in this article to segment a true color image i... more A novel neuro-fuzzy-genetic approach is presented in this article to segment a true color image into different color levels. A MUSIG activation function induces multiscaling capabilities in a parallel self organizing neural network (PSONN) architecture. The function however resorts to equal and fixed class responses, assuming the homogeneity of image information content. In the proposed approach, genetic algorithm has been used to generate optimized class responses of the MUSIG activation function. Subsequently, the color images are segmented by applying the resultant optimized multilevel sigmoidal (OptiMUSIG) activation function. Comparative results of segmentation of two real life true color images indicate better segmentation efficiency of the OptiMUSIG activation function over the standard MUSIG activation function.
Automatic data clustering through determination of optimal number of clusters from the data conte... more Automatic data clustering through determination of optimal number of clusters from the data content, is a challenging proposition. Lack of knowledge regarding the underlying data distribution poses constraints in proper determination of the inherent number of clusters. A differential evolution (DE) algorithm based approach for the determination of the optimal number of clusters from the data under consideration, is presented in this article. The optimum number of clusters obtained by the algorithm is further validated by means of a proposed fuzzy intercluster hostility index between the different clusters thus obtained. Applications of the proposed approach on clustering of real life gray level images indicate encouraging results. The proposed method is also compared with the classical DE (which operates with a known number of classes) and the automatic clustering DE (ACDE) algorithms.
A multilevel gray scale image can quite efficiently be segmented by the multilevel sigmoidal (MUS... more A multilevel gray scale image can quite efficiently be segmented by the multilevel sigmoidal (MUSIG) activation function based on equal and fixed class responses, ignoring the heterogeneity of image information content. The optimized version of MUSIG (OptiMUSIG) activation function can be generated with the optimized class responses from the image content and can be used effectively to segment the multilevel gray scale images. These methods may or may not generate a good quality segmented image as the segmentation criteria of these methods are based on single segmentation evaluation criterion. This article proposed a self supervised image segmentation method by a non-dominated sorting genetic algorithm-II (NSGA-II) based optimized MUSIG (OptiMUSIG) activation function with a multi layer self organizing neural network (MLSONN) architecture to segment a multilevel gray scale intensity images. Some standard objective functions are applied in this proposed method to measure the quality of the segmented images. These functions form the multiple objective criteria of the NSGA-II based image segmentation method.
International Journal of Parallel, Emergent and Distributed Systems, 2011
The conventional multilevel sigmoidal (MUSIG) activation function is efficient in segmenting mult... more The conventional multilevel sigmoidal (MUSIG) activation function is efficient in segmenting multilevel images. The function uses equal and fixed class responses, thereby ignoring the heterogeneity of image information content. In this article, a novel approach for generating optimised class responses of the MUSIG activation function is proposed so that image content heterogeneity can be incorporated in the segmentation procedure. Four different types of objective function are used to measure the quality of the segmented images in the proposed genetic algorithm-based optimisation method. Results of segmentation of one synthetic and two real-life images by the proposed optimised MUSIG (OptiMUSIG) activation function with optimised class responses show better performances over the conventional MUSIG counterpart with equal and fixed responses. Comparative studies with the standard fuzzy c-means (FCM) algorithm, efficient in clustering of multidimensional data, also reveal better performances of the proposed function.
An optimized multilevel sigmoidal (OptiMUSIG) activation function for segmentation of multilevel ... more An optimized multilevel sigmoidal (OptiMUSIG) activation function for segmentation of multilevel images is presented. The OptiMUSIG activation function is generated from the optimized class boundaries of input images. Results of application of the function with fixed and variable thresholding mechanisms are demonstrated on two real life images. The proposed OptiMUSIG activation function is found to outperform the conventionalMUSIG activation function using both fixed and variable thresholds.
A multilevel sigmoidal (MUSIG) activation function is efficient in segmenting multilevel images. ... more A multilevel sigmoidal (MUSIG) activation function is efficient in segmenting multilevel images. The function uses equal and fixed class responses, assuming the homogeneity of image information content. In this article, a novel approach for generating optimized class responses of the MUSIG activation function, is proposed. Three different types of objective function are used to measure the quality of the segmentation in the proposed genetic algorithm based optimization method. Results of segmentation of two real life images by the optimized MUSIG (OptiMUSIG) activation function with optimized class responses show better performances over the MUSIG activation function with equal and fixed responses.
A novel neuro-fuzzy-genetic approach is presented in this article to segment a true color image i... more A novel neuro-fuzzy-genetic approach is presented in this article to segment a true color image into different color levels. A MUSIG activation function induces multiscaling capabilities in a parallel self organizing neural network (PSONN) architecture. The function however resorts to equal and fixed class responses, assuming the homogeneity of image information content. In the proposed approach, genetic algorithm has been used to generate optimized class responses of the MUSIG activation function. Subsequently, the color images are segmented by applying the resultant optimized multilevel sigmoidal (OptiMUSIG) activation function. Comparative results of segmentation of two real life true color images indicate better segmentation efficiency of the OptiMUSIG activation function over the standard MUSIG activation function.
Automatic data clustering through determination of optimal number of clusters from the data conte... more Automatic data clustering through determination of optimal number of clusters from the data content, is a challenging proposition. Lack of knowledge regarding the underlying data distribution poses constraints in proper determination of the inherent number of clusters. A differential evolution (DE) algorithm based approach for the determination of the optimal number of clusters from the data under consideration, is presented in this article. The optimum number of clusters obtained by the algorithm is further validated by means of a proposed fuzzy intercluster hostility index between the different clusters thus obtained. Applications of the proposed approach on clustering of real life gray level images indicate encouraging results. The proposed method is also compared with the classical DE (which operates with a known number of classes) and the automatic clustering DE (ACDE) algorithms.
A multilevel gray scale image can quite efficiently be segmented by the multilevel sigmoidal (MUS... more A multilevel gray scale image can quite efficiently be segmented by the multilevel sigmoidal (MUSIG) activation function based on equal and fixed class responses, ignoring the heterogeneity of image information content. The optimized version of MUSIG (OptiMUSIG) activation function can be generated with the optimized class responses from the image content and can be used effectively to segment the multilevel gray scale images. These methods may or may not generate a good quality segmented image as the segmentation criteria of these methods are based on single segmentation evaluation criterion. This article proposed a self supervised image segmentation method by a non-dominated sorting genetic algorithm-II (NSGA-II) based optimized MUSIG (OptiMUSIG) activation function with a multi layer self organizing neural network (MLSONN) architecture to segment a multilevel gray scale intensity images. Some standard objective functions are applied in this proposed method to measure the quality of the segmented images. These functions form the multiple objective criteria of the NSGA-II based image segmentation method.
International Journal of Parallel, Emergent and Distributed Systems, 2011
The conventional multilevel sigmoidal (MUSIG) activation function is efficient in segmenting mult... more The conventional multilevel sigmoidal (MUSIG) activation function is efficient in segmenting multilevel images. The function uses equal and fixed class responses, thereby ignoring the heterogeneity of image information content. In this article, a novel approach for generating optimised class responses of the MUSIG activation function is proposed so that image content heterogeneity can be incorporated in the segmentation procedure. Four different types of objective function are used to measure the quality of the segmented images in the proposed genetic algorithm-based optimisation method. Results of segmentation of one synthetic and two real-life images by the proposed optimised MUSIG (OptiMUSIG) activation function with optimised class responses show better performances over the conventional MUSIG counterpart with equal and fixed responses. Comparative studies with the standard fuzzy c-means (FCM) algorithm, efficient in clustering of multidimensional data, also reveal better performances of the proposed function.
An optimized multilevel sigmoidal (OptiMUSIG) activation function for segmentation of multilevel ... more An optimized multilevel sigmoidal (OptiMUSIG) activation function for segmentation of multilevel images is presented. The OptiMUSIG activation function is generated from the optimized class boundaries of input images. Results of application of the function with fixed and variable thresholding mechanisms are demonstrated on two real life images. The proposed OptiMUSIG activation function is found to outperform the conventionalMUSIG activation function using both fixed and variable thresholds.
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