A Joint Entropy for Image Segmentation Based on Quasi Opposite Multiverse Optimization
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A Joint Entropy for Image Segmentation Based on Quasi Opposite Multiverse Optimization Mausam Chouksey1
· Rajib Kumar Jha1
Received: 4 October 2019 / Revised: 30 July 2020 / Accepted: 9 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Image segmentation is the initial task in image processing which is extensively utilized in object recognition and detection. In the field of image segmentation, multilevel thresholding is one of the leading methods. Though, the computational cost of this method scales exponentially as the number of the threshold value increases, which directs to exercise of optimization method to determine the optimal value of the thresholds. In this article, a newly modified algorithm called quasi opposite multiverse optimization (QOMVO) is proposed. The proposed QOMVO is based on quasi opposite based learning and multiverse optimization (MVO) algorithm. The quasi opposite based learning helps to improve the exploration phase of QOMVO. QOMVO is coupled with a new proposed entropy called Joint entropy (Renyi-Tsalii) to perform image segmentation by finding the optimal threshold value. The outcome of the proposed algorithm is compared with other evolutionary algorithm based on objective function value, feature similarity index, structural similarity index , quality index based on local variance, uniformity, normalized absolute error and computational time. A non-parametric test called the Wilcoxon test is done to justify the response of these parameters. Along with comparing with other algorithms, a comparison has also been made with other entropy, i.e. Renyi’s and Tsallis. The experimental outcomes confirmed that the proposed algorithm provides more reliable results than other existing methods. Keywords Image segmentation · Joint entropy · Multiverse optimization (MVO) · Multilevel thresholding · Quasi opposite based learning · Renyi’s and Tsallis entropy · Wilcoxon rank sum test
Mausam Chouksey
[email protected] Rajib Kumar Jha [email protected] 1
Department of Electrical Engineering, Indian Institute of Technology Patna, Bihta 801103, Bihar, India
Multimedia Tools and Applications
1 Introduction All the computer vision system and pattern recognition applications involved image segmentation as a vital preprocessing step for video surveillance [14], object recognition [6], satellite image processing [4, 5] and medical imaging [22, 30, 34, 35] . Image segmentation is most widely used primary step in image processing and computer vision which can be defined as segregating an image into non-overlapping, disjoint and homogeneous sets of pixels with unique properties such as intensity, colour or contours [15]. Basically, Segmentation decomposes an image into meaningful sections which correspond to different real-world objects from complex scenes. For practical application, robustness and highquality image segmentation are essential to enhance the quality of feature extraction and classification in computer vision and image processing. Fro
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