Superpixel segmentation is a foundation for many higher-level computer vision tasks, such as image segmentation, object recognition, and scene understanding. Existing graph-based superpixel segmentation methods typically concentrate on the relationships between a given pixel and its directly adjacent pixels while overlooking the influence of non-adjacent pixels. These approaches do not fully leverage the global information in the graph, leading to suboptimal segmentation quality. To address this limitation, we present SIT-HSS, a hierarchical superpixel segmentation method based on structural information theory. Specifically, we first design a novel graph construction strategy that incrementally explores the pixel neighborhood to add edges based on 1-dimensional structural entropy (1D SE). This strategy maximizes the retention of graph information while avoiding an overly complex graph structure. Then, we design a new 2D SE-guided hierarchical graph partitioning method, which iteratively merges pixel clusters layer by layer to reduce the graph’s 2D SE until a predefined segmentation scale is achieved. Experimental results on three benchmark datasets demonstrate that the SIT-HSS performs better than state-of-the-art unsupervised superpixel segmentation algorithms. The source code is available at https://github.com/SELGroup/SIT-HSS.

Graph kernels have been regarded as a successful tool for handling a variety of graph applications since they were proposed. However, most of the proposed graph kernels are based on the R-convolution framework, which decomposes graphs into a set of substructures at the same abstraction level and compares all substructure pairs equally; these methods inherently overlook the utility of the hierarchical structural information embedded in graphs. In this paper, we proposeHierarchicalAbstractingGraphKernels (HAGK), a novel set of graph kernels that compare graphs’ hierarchical substructures to capture and utilize the latent hierarchical structural information fully. Instead of generating non-structural substructures, we reveal each graph’s hierarchical substructures by constructing itshierarchical abstracting, specifically, the hierarchically organized nested node sets adhering to the principle of structural entropy minimization. To compare a pair of hierarchical abstractings, we propose two novel substructure matching approaches,Local Optimal Matching(LOM) andPriority Ordering Matching(POM), to find appropriate matching between the substructures by different strategies recursively. Extensive experiments demonstrate that the proposed kernels are highly competitive with the existing state-of-the-art graph kernels, and verify that the hierarchical abstracting plays a significant role in the improvement of the kernel performance.