NDLI: An improved multilevel thresholding approach based modified bacterial foraging optimization

Content Provider	Springer Nature Link
Author	Tang, Kezong Xiao, Xuan Wu, Jun Yang, Jingyu Luo, Limin
Copyright Year	2016
Abstract	In this work, a multilevel thresholding approach that uses modified bacterial foraging optimization (MBFO) is presented for enhancing the applicability and practicality of optimal thresholding techniques. First, the diversity of solutions is considered during the reproduction step. Each weak bacterium randomly selects a strong bacterium from the healthiest bacteria, attempts to reach a location near the chosen strong bacterium, and maintains the same direction. Particle swarm optimization is subsequently incorporated into each chemotactic step to strengthen the global searching capability and quicken the convergence rate of the bacterial foraging algorithm. Finally, the optimal thresholds are obtained by maximizing the Tsallis thresholding functions using the proposed MBFO algorithm. The performance of the proposed algorithm in solving complex stochastic optimization problems is compared with other popular approaches such as a bacterial foraging algorithm, particle swarm optimization algorithm, and genetic algorithm. Experimental results show that the optimal thresholds produced using MBFO require less computation time. The devised algorithm generates more stable results, and the proposed method performs better than the other algorithms in terms of multilevel thresholding. In addition, MBFO method can achieve significantly better results than other compare algorithms on a set of benchmark functions.
Starting Page	214
Ending Page	226
Page Count	13
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	46
Issue Number	1
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2016-08-15
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image segmentation Thresholding Tsallis entropy Bacterial foraging Particle swarm optimization Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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