Kernels on Attributed Pointsets with Applications

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Mehul Parsana, Sourangshu Bhattacharya, Chiru Bhattacharya, K. Ramakrishnan


This paper introduces kernels on attributed pointsets, which are sets of vectors embedded in an euclidean space. The embedding gives the notion of neighborhood, which is used to define positive semidefinite kernels on pointsets. Two novel kernels on neighborhoods are proposed, one evaluating the attribute similarity and the other evaluating shape similarity. Shape similarity function is motivated from spectral graph matching techniques. The kernels are tested on three real life applications: face recognition, photo album tagging, and shot annotation in video sequences, with encouraging results.