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

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.