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Image Segmentation Using Electromagnetic Field Optimization (EFO) in E-Commerce Applications

Image Segmentation Using Electromagnetic Field Optimization (EFO) in E-Commerce Applications

Pankaj Upadhyay, Jitender Kumar Chhabra
Copyright: © 2019 |Volume: 10 |Issue: 3 |Pages: 16
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781522566625|DOI: 10.4018/IJISMD.2019070105
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MLA

Upadhyay, Pankaj, and Jitender Kumar Chhabra. "Image Segmentation Using Electromagnetic Field Optimization (EFO) in E-Commerce Applications." IJISMD vol.10, no.3 2019: pp.76-91. http://doi.org/10.4018/IJISMD.2019070105

APA

Upadhyay, P. & Chhabra, J. K. (2019). Image Segmentation Using Electromagnetic Field Optimization (EFO) in E-Commerce Applications. International Journal of Information System Modeling and Design (IJISMD), 10(3), 76-91. http://doi.org/10.4018/IJISMD.2019070105

Chicago

Upadhyay, Pankaj, and Jitender Kumar Chhabra. "Image Segmentation Using Electromagnetic Field Optimization (EFO) in E-Commerce Applications," International Journal of Information System Modeling and Design (IJISMD) 10, no.3: 76-91. http://doi.org/10.4018/IJISMD.2019070105

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Abstract

Image recognition plays a vital role in image-based product searches and false logo identification on e-commerce sites. For the efficient recognition of images, image segmentation is a very important and is an essential phase. This article presents a physics-inspired electromagnetic field optimization (EFO)-based image segmentation method which works using an automatic clustering concept. The proposed approach is a physics-inspired population-based metaheuristic that exploits the behavior of electromagnets and results into a faster convergence and a more accurate segmentation of images. EFO maintains a balance of exploration and exploitation using the nature-inspired golden ratio between attraction and repulsion forces and converges fast towards a globally optimal solution. Fixed length real encoding schemes are used to represent particles in the population. The performance of the proposed method is compared with recent state of the art metaheuristic algorithms for image segmentation. The proposed method is applied to the BSDS 500 image data set. The experimental results indicate better performance in terms of accuracy and convergence speed over the compared algorithms.

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