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.
multi-level thresholding is one of the most popular techniques in imagesegmentation. However, selecting the optimal thresholds with high accuracy and efficiency is still challenging. In this paper, a novel multi-leve...
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multi-level thresholding is one of the most popular techniques in imagesegmentation. However, selecting the optimal thresholds with high accuracy and efficiency is still challenging. In this paper, a novel multi-level thresholding method using between-class variance (Otsu) based on an improved invasive weed optimization algorithm (FIWO) is proposed. In the FIWO algorithm, the forking technique of the lightning search algorithm is introduced to guarantee the quality of the initial population and to enhance the exploration of the algorithm. In addition, the current best solution swing operation is used to obtain the optimal thresholds with a fast convergence rate. Comparative experiments are carried out to test the performance of FIWO. The results show that the proposed FIWO algorithm is able to achieve better segmented images with fewer iterations than those of the simulated annealing algorithm, gravitational search algorithm, whale optimization algorithm and traditional invasive weed optimization algorithm.
In this paper, we have proposed a fusion-based context-sensitive Masi energy curve model for multi-level thresholding exploiting cuttlefish algorithm (CFA). The proposed algorithm is simple and very efficient for the ...
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In this paper, we have proposed a fusion-based context-sensitive Masi energy curve model for multi-level thresholding exploiting cuttlefish algorithm (CFA). The proposed algorithm is simple and very efficient for the task of color imagesegmentation. Although Masi entropy exploits the additive/non-extensive information with the aid of a concordant entropic parameter, the performance is observed to be poor in the case of color imagesegmentation. Improved results can be obtained by using the concept of energy curve with Masi entropy at the cost of increased computational cost while selecting the suitable thresholds. To overcome the aforementioned drawbacks as well as to increase the quality of the segmented image, a simple multi-level thresholding method is proposed in this paper. The proposed color imagesegmentation scheme exploits the concept of local contrast fusion along with CFA to resolve the aforementioned issues. In order to prove the effectiveness of the proposed scheme, experimental evaluations on standard daily-life color images have been reported in this paper. The experimental outputs demonstrate that fusion-based multi-level thresholding is better than the existing dominant segmentation methods.
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.
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