Image segmentation based on improved density peak clustering

Abstract

Density peaks clustering(DPC) has been applied in the field of image segmentation. However, DPC only considers the global spatial information, but not the local spatial information. When a point is incorrectly allocated, the rest points will have a chain reaction, resulting in being divided into the wrong cluster. Therefore, we propose an image segmentation method based on superpixel and improved DPC. This method can fully consider local spatial information and is more robust. Firstly, an improved DPC is proposed to improve the clustering accuracy, and a new image segmentation algorithm is proposed by combining the improved DPC with superpixel. We compare this algorithm with other three algorithms in BSDS500 dataset. Four indicators of these algorithms show that the algorithm is superior to the other three algorithms.