LAUSR

Abstract In computer vision, superpixel segmentation has been widely used as a very important preprocessing to reduce the number of image primitives for subsequent image processing tasks. To improve the segmentation accuracy and the robustness to noise, a hierarchical multi-level segmentation framework is developed in this paper. First, original image is initially segmented by a local information based simple liner iterative clustering (LI-SLIC) method. Then, the initial generated superpixels are further segmented hierarchically by LI-SLIC to ensure that all pixels contained within each superpixel belong to a same object. Finally, to eliminate over-segmentation and reduce the number of superpixels, adjacent superpixels belonging to a same object are merged based on the probability distribution similarity. The proposed method does not require setting the seeds or number of the superpixels to be generated in advance, which can segment image into an appropriate number of superpixels without under- or over- segmentation automatically according to its content. Experiments are conducted on two public datasets Berkeley and 3Dircadb, and the results demonstrate that our method is more effective and accurate than many existing superpixel methods and shows a great advantage in dealing with images corrupted by various noises.