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Abstract
Image processing is one example of digital media. It consists of a set of operations to handle an image. Image segmentation is among its main important operations. It involves dividing the image into several parts or regions to extract vital information or identify relevant objects. Many techniques of artificial intelligence, including bio-inspired algorithms, have been used in this regard. This article collected the state-of-the-art studies presenting image-segmentation techniques combined with four bio-inspired algorithms including particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), and artificial bee colonies (ABC). This research work aimed at showing the importance of image segmentation and its combination with these algorithms. This article provides insights on how these algorithms are adapted to image-segmentation combinatorial problems, which assist researchers to start the first hands-on application. It also discusses their setting parameters and the highly used algorithms such as PSO, GA, ACO, and ABC. The article presents new research directions in image segmentation based on bio-inspired algorithms.
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Singh S, Mittal N, Singh H. A multilevel thresholding algorithm using HDAFA for image segmentation. Soft comput 2021. [DOI: 10.1007/s00500-021-05956-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Singh S, Mittal N, Singh H. A multilevel thresholding algorithm using LebTLBO for image segmentation. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04989-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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Vijh S, Sharma S, Gaurav P. Brain Tumor Segmentation Using OTSU Embedded Adaptive Particle Swarm Optimization Method and Convolutional Neural Network. DATA VISUALIZATION AND KNOWLEDGE ENGINEERING 2020. [DOI: 10.1007/978-3-030-25797-2_8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Upadhyay P, Chhabra JK. Image Segmentation Using Electromagnetic Field Optimization (EFO) in E-Commerce Applications. INTERNATIONAL JOURNAL OF INFORMATION SYSTEM MODELING AND DESIGN 2019. [DOI: 10.4018/ijismd.2019070105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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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Affiliation(s)
- Pankaj Upadhyay
- National Institute of Technology Kurukshetra, Kurukshetra, India
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Improved Hybrid Bat Algorithm with Invasive Weed and Its Application in Image Segmentation. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019. [DOI: 10.1007/s13369-019-03874-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Hatta NM, Zain AM, Sallehuddin R, Shayfull Z, Yusoff Y. Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017). Artif Intell Rev 2018. [DOI: 10.1007/s10462-018-9634-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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