Region-adaptive superpixel segmentation algorithm based on density clustering

In addressing the issue of unstable boundary adherence in traditional superpixel segmentation algorithms, this paper proposes a novel region-adaptive superpixel segmentation algorithm based on density clustering for computer vision research. Firstly, during the initialization of superpixels, a region-adaptive initialization method is employed based on the complexity of image content. This method takes into consideration the diversity of image content, thereby reducing redundancy in the initialization to a certain extent. Secondly, within the initialized superpixel regions, two pixels properties, namely distance and membership relationship, are computed. Pixels with higher local density are chosen as seed points, and their membership relationships are marked. Finally, seed points closest to the pixels are selected in the membership relationships, and labels are assigned to the pixels, completing the superpixel segmentation of the image. Experimental results, compared with six advanced algorithms, demonstrate that the proposed algorithm exhibits superior performance in terms of boundary recall rate, under-segmentation error rate, and accuracy while maintaining a higher level of edge adherence.