Kernels on Attributed Pointsets with Applications
Abstract
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.
Cite
Text
Parsana et al. "Kernels on Attributed Pointsets with Applications." Neural Information Processing Systems, 2007.Markdown
[Parsana et al. "Kernels on Attributed Pointsets with Applications." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/parsana2007neurips-kernels/)BibTeX
@inproceedings{parsana2007neurips-kernels,
title = {{Kernels on Attributed Pointsets with Applications}},
author = {Parsana, Mehul and Bhattacharya, Sourangshu and Bhattacharya, Chiru and Ramakrishnan, K.},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {1129-1136},
url = {https://mlanthology.org/neurips/2007/parsana2007neurips-kernels/}
}