Shape classification with a vertex clustering graph kernel

Lu Bai, Lixin Cui, Yue Wang, Xin Jin, Xiao Bai, Edwin R. Hancock

Abstract: Graph kernels are powerful tools for structural analysis in computer vision. Unfortunately, most existing state-of-the-art graph kernels ignore the locational or structural correspondence information between graphs, based on the visual background. This drawback influences the performance of existing kernels for computer vision based classification problems, e.g., classification of shapes, point clouds and digital images. The aim of this paper is to address the problem with existing kernels, by developing a novel vertex clustering graph kernel. We show that this kernel not only overcomes the shortcoming of ignoring correspondence information between isomorphic substructures that arises in most existing graph kernels, but also guarantees the transitivity between the correspondence information. Our kernel can easily outperform state-of-the-art graph kernels in terms of classification accuracy on standard shape based graph datasets.