An Improved Method of Linear Spectral Clustering

Superpixel segmentation is a popular image preprocessing technology in image processing. Among the various methods used to calculate uniform superpixel, the performance of linear spectral clustering (LSC) is better than the state-of-the-art superpixel segmentation algorithms. However, this method is slow on images and has low accuracy on non-convex images. In order to improve this problem, we propose a subsampled clustering method that can accelerate LSC. Meanwhile, this paper presents an improved distance measurement method based on non-convex image features and Manhattan distance, which can achieve high accuracy on non-convex images. The proposed method is evaluated on the BSDS500 dataset. The experimental results confirmed that this method runs faster than LSC, and at the same time produces almost the same superpixel segmentation accuracy on the images. In addition, the proposed method improves the accuracy of superpixel segmentation on non-convex images.