BibTeX
@MISC{Kikuchi_votingbased,
author = {Kei Kikuchi and Seiji Hotta},
title = {Voting based Video Classification Using Clustering and Learning},
year = {}
}
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Abstract
In this paper, we propose video classification using lin-ear manifolds (affine subspaces). In our method, we rep-resent videos belonging to a same class by several linear manifolds using k-varieties clustering. When a test video is given, each frame of it votes for the class to which its nearest linear manifold belongs. According to this voting, the test video is classified into the class that achieves the majority votes. For improving accuracy, a way of adopting generalized learning vector quantization for our video clas-sification is also presented. The performance of our video classification is verified with experiments on short videos downloaded from Web. 1
