Multiscale Graph Construction Using Non-local Cluster Features

• URL: http://arxiv.org/abs/2411.08371v1
• Date: Wed, 13 Nov 2024 06:42:03 GMT
• Title: Multiscale Graph Construction Using Non-local Cluster Features
• Authors: Reina Kaneko, Hayate Kojima, Kenta Yanagiya, Junya Hara, Hiroshi Higashi, Yuichi Tanaka,
• Abstract summary: We consider graph and node-wise features simultaneously for multiscale clustering of a graph. In experiments on multiscale image and point cloud segmentation, we demonstrate the effectiveness of the proposed method.
• Score: 10.922757310575307
• License:
• Abstract: This paper presents a multiscale graph construction method using both graph and signal features. Multiscale graph is a hierarchical representation of the graph, where a node at each level indicates a cluster in a finer resolution. To obtain the hierarchical clusters, existing methods often use graph clustering; however, they may ignore signal variations. As a result, these methods could fail to detect the clusters having similar features on nodes. In this paper, we consider graph and node-wise features simultaneously for multiscale clustering of a graph. With given clusters of the graph, the clusters are merged hierarchically in three steps: 1) Feature vectors in the clusters are extracted. 2) Similarities among cluster features are calculated using optimal transport. 3) A variable $k$-nearest neighbor graph (V$k$NNG) is constructed and graph spectral clustering is applied to the V$k$NNG to obtain clusters at a coarser scale. Additionally, the multiscale graph in this paper has \textit{non-local} characteristics: Nodes with similar features are merged even if they are spatially separated. In experiments on multiscale image and point cloud segmentation, we demonstrate the effectiveness of the proposed method.

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