Title: SLIC-SSA: an image segmentation method based on superpixel and sparrow search algorithm

Authors: Hao Li; Hong Wen; Jia Li; Lijun Xiao

Addresses: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China

Abstract: Clustering algorithms are widely used in image segmentation due to their universality. However, the methods based on clustering algorithms are sensitive to noise and readily fall into local optimum. To address these issues, we propose an image segmentation method (SLIC-SSA) based on superpixel method and sparrow search algorithm. Firstly, the presegmentation result is obtained by superpixel method. Due to the use of local spatial information, the influence of noise can be reduced. Then, the clustering algorithm based on sparrow search algorithm is performed on superpixel image to complete the segmentation. To improve the quality of the results, the chaotic strategy is used to initialise the population. A fitness function is proposed to ensure the similarity within the cluster and the difference between the clusters. Experiments on real images show that the proposed method can obtain better results than comparative methods. Meanwhile, time consumption can be reduced.

Keywords: clustering; image segmentation; sparrow search; superpixel; swarm intelligence optimisation.

DOI: 10.1504/IJCSE.2024.137288

International Journal of Computational Science and Engineering, 2024 Vol.27 No.2, pp.182 - 194

Received: 21 Oct 2022
Accepted: 16 Dec 2022
Published online: 11 Mar 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article