Threshold-Based Image Segmentation through an Improved Particle Swarm Optimisation

Image segmentation plays a critical role in the process of object recognition in digital image processing. Multilevel threshold-based segmentation is one of the most popular image segmentation techniques. This paper proposes a new multilevel threshold-based image segmentation approach on the basis of the new particle swarm optimisation with wavelet mutation. Unlike the conventional particle swarm optimisation (PSO), our new algorithm distinguishes itself by having the following advantages: 1) Faster convergence rate; 2) Multi-dimensional data processing. The basic idea in this paper is to optimise the multilevel thresholds for the images and therefore to reach the goal of the total entropy of the image being maximized. The new algorithm leads to better optimised thresholds and produces more accurate segmentation results for images with multiple attributes. The trade-off between good search stability and cheaper computational cost is well balanced. By comparing with the Genetic Algorithm(GA) and an existing PSO-based image scheme called HCOCLPSO, the experimental results demonstrate the proposed scheme outperforms and very promising in terms of faster convergence rate, better exploration and exploitation capacities in the segmentation process with the reduced computational time.