Title: An object classification method based on the improved bacterial foraging optimisation algorithm

Authors: Zhigao Zeng; Lianghua Guan; Shengqiu Yi; Yanhui Zhu; Qiang Liu; Qi Tong; Sanyou Zeng

Addresses: College of Computer and Communication, Hunan University of Technology, Hunan 412007, China; Intelligent Information Perception and Processing Technology, Hunan Province Key Laboratory, Hunan, China ' College of Computer and Communication, Hunan University of Technology, Hunan 412007, China ' College of Computer and Communication, Hunan University of Technology, Hunan 412007, China ' College of Computer and Communication, Hunan University of Technology, Hunan 412007, China ' College of Computer and Communication, Hunan University of Technology, Hunan 412007, China ' College of Computer and Communication, Hunan University of Technology, Hunan 412007, China ' Department of Computer Science, China University of GeoSciences, Wuhan, Hubei, China

Abstract: This paper proposes a new object classification method based on an improved bacterial foraging optimisation algorithm. Firstly, a dynamic step size is used instead of the fixed step size of the chemotaxis. Secondly, the fixed elimination-dispersal probability is replaced by the dynamic probability. Features are extracted to distinguish the objects, such as pedestrians, cars and pets. Ultimately, all the objects are classified using the improved bacterial foraging optimisation algorithm. The experimental results prove that the effectiveness of the object classification method proposed in this paper is better than that of other algorithms.

Keywords: object classification; BFO; bacterial foraging optimisation; feature extraction.

DOI: 10.1504/IJWMC.2017.084175

International Journal of Wireless and Mobile Computing, 2017 Vol.12 No.2, pp.166 - 173

Available online: 08 May 2017 *

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