Hierarchical Superpixel Segmentation via Structural Information Theory

• URL: http://arxiv.org/abs/2501.07069v1
• Date: Mon, 13 Jan 2025 05:39:43 GMT
• Title: Hierarchical Superpixel Segmentation via Structural Information Theory
• Authors: Minhui Xie, Hao Peng, Pu Li, Guangjie Zeng, Shuhai Wang, Jia Wu, Peng Li, Philip S. Yu,
• Abstract summary: Superpixel segmentation is a foundation for many higher-level computer vision tasks. We present SIT-HSS, a hierarchical superpixel segmentation method based on structural information theory. We show that SIT-HSS performs better than state-of-the-art unsupervised superpixel segmentation algorithms.
• Score: 48.488598357738674
• License:
• Abstract: 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 \url{https://github.com/SELGroup/SIT-HSS}.

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