基于非局部特征的多尺度图构建方法，能捕捉远距离相似节点
Multiscale Graph Construction Using Non-local Cluster Features
- 融合图结构与节点特征，分三步进行层次聚类
- 通过最优传输计算簇间相似性，构建可变邻域图并聚类
- 支持非局部合并，适合图像与点云多尺度分割任务
本文提出一种结合图结构与节点特征的多尺度图构建方法。多尺度图是以层次化方式表示图，每一层的节点对应更细粒度下的簇。现有方法常仅依赖图聚类，忽略信号差异，导致无法发现具有相似特征但空间分离的簇。为此，本文同时考虑图结构和节点特征进行多尺度聚类。给定初始簇后，分三步进行层级合并：1）提取各簇的特征向量；2）使用最优传输计算簇间相似性；3）构建可变邻域图（V$k$NNG），并对其应用图谱聚类，获得粗粒度簇。所提方法具有非局部特性：即使节点在空间上相隔较远，只要特征相似就会被合并。在多尺度图像与点云分割任务上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。