Unsupervised image segmentation based on multidimensional particle swarm optimization
Unsupervised image segmentation based on multidimensional particle swarm optimization
- Author(s): Lin Wang ; Wanxu Zhang ; Dong Wang ; Bo Jiang
- DOI: 10.1049/cp.2015.0938
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- Author(s): Lin Wang ; Wanxu Zhang ; Dong Wang ; Bo Jiang Source: 6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN 2015), 2015 page ()
- Conference: 6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN 2015)
- DOI: 10.1049/cp.2015.0938
- ISBN: 978-1-78561-046-2
- Location: Beijing, China
- Conference date: 20-23 Nov. 2015
- Format: PDF
An unsupervised image segmentation method based on multidimensional (MD) particle swarm optimization (PSO) is proposed in this paper. Firstly, a clustering-based nonlinear objective function of unsupervised image segmentation is established according to Turi's validity index. Secondly, MD PSO algorithm is adopted to minimize the objective function to seek the optimal number and cluster centers of segmented regions simultaneously. Finally, global best (GB) position of swam in each dimension is modified to avoid being trapped in local optima. Experimental results valid the performance of the proposed image segmentation algorithm.
Inspec keywords: particle swarm optimisation; pattern clustering; image segmentation; minimisation
Subjects: Optimisation techniques; Optical, image and video signal processing; Computer vision and image processing techniques; Optimisation techniques
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