In this paper, a novel context sensitive energy curve based Masi entropy for imagesegmentation using moth swarm algorithm (MSA) has been proposed. Although Masi entropy deals with complete probability distribution fo...
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In this paper, a novel context sensitive energy curve based Masi entropy for imagesegmentation using moth swarm algorithm (MSA) has been proposed. Although Masi entropy deals with complete probability distribution for imagesegmentation but the performance is not satisfactory. However, better results can be obtained using the concept of energy curve for Masi entropy, but it consumes more time and also the complexity level for selecting suitable thresholds is high. MSA is a newly developed stochastic meta-heuristic optimization algorithm introduced after observing, mimicking and modeling the life cycle of moth swarm. It is used to simplify the problem of extensive exploration for finding the optimum threshold values and to increase the quality of the images. Experiments on standard daily-life color images are showed to establish the usefulness of the presented approach. The Energy-Masi-MSA technique is examined intensively regarding visual quality and quantitative matrices are considered to evaluate the results of the Energy-Masi-MSA scheme compared to existing methods. Unlike other meta-heuristic algorithms used for thresholding operations, MSA provides a higher performance regarding threshold quality and low computational cost. Experimental data boosts the use of MSA for energy curve based thresholding with Masi entropy.
The segmentation process is considered the significant step of an image processing system due to its extreme inspiration on the subsequent image analysis. Out of various approaches, thresholding is one of the most pop...
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The segmentation process is considered the significant step of an image processing system due to its extreme inspiration on the subsequent image analysis. Out of various approaches, thresholding is one of the most popular schemes for imagesegmentation. In segmentation, image pixels are arranged in various regions based on their intensity levels. In this paper, a straightforward and efficient fusion-based fuzzy model for multilevel color imagesegmentation using grasshopper optimization algorithm (GOA) has been proposed. Thresholding based segmentation lacks accuracy in segmenting the ambiguous images due to their complex characteristics, uncertainties and inherent fuzziness. However, the fuzzy entropy resolves these problems, but it is unable for segmenting at higher levels and also the complexity level for selecting suitable thresholds is high. The selection of metaheuristic GOA reduces this problem by selecting optimal threshold values. Therefore, to increase the quality of the segmented image, a simple and effective multilevel thresholding method is exploited by using the concept of fusion which is based on the local contrast. Experimental outputs demonstrate that fusion-based multilevel thresholding is better than most specific segmentation methods and can be validated by comparing the different numerical parameters. Experiments on standard daily-life color and satellite images are conducted to prove the effectiveness of the proposed scheme. (C) 2019 Elsevier B.V. All rights reserved.
We propose a novel multi-level thresholding method for unsupervised separation between objects and background from a natural color image using the concept of the minimum cross entropy (MCE). MCE based thresholding tec...
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We propose a novel multi-level thresholding method for unsupervised separation between objects and background from a natural color image using the concept of the minimum cross entropy (MCE). MCE based thresholding techniques are widely popular for segmenting grayscale images. Color imagesegmentation is still a challenging field as it involves 3-D histogram unlike the 1-D histogram of grayscale images. Effectiveness of entropy based multi-level thresholding for color image is yet to be explored and this paper presents a humble contribution in this context. We have used differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, to improve the computation time and robustness of the proposed algorithm. The performance of DE is also investigated extensively through comparison with other well-known nature inspired global optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The proposed method is evaluated by comparing it with seven other prominent algorithms both qualitatively and quantitatively using a well known benchmark suite - the Barkley segmentation Dataset (BSDS300) with 300 distinct images. Such comparison reflects the efficiency of our algorithm (C) 2014 Published by Elsevier B.V.
Due to the increasing demand and offer of tile technology, the next generation of the image file formats will be more likely to store and retrieve images based oil their semantic content. Thus, an image should be segm...
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ISBN:
(纸本)0819439932
Due to the increasing demand and offer of tile technology, the next generation of the image file formats will be more likely to store and retrieve images based oil their semantic content. Thus, an image should be segmented into "meaningful' regions, each of which corresponds to all object and/or background. In this study., we propose a scheme for multi-level image segmentation, based oil a simple descriptor, called "the closest color in the same neighborhood". Tile proposed scheme generates a stack of images without using arty segmentation threshold. The stack of images is hierarchically ordered in a uniformity tree. The uniformity tree is, then, associated with a semantic tree, which is built by tile user for content based representation. The experiments indicate superior results for retrieving images, which consist of fen objects and a background.
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