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VRules: an effective association-based classifier for videos

Published: 13 November 2004 Publication History

Abstract

Video classification is an important step towards multimedia understanding. Most state-of-the-art approaches which apply HMM to capture the temporal information of videos have the limitation by assuming that the current state of a video depends only on the immediate previous state. Nevertheless, this assumption may not hold for videos of various categories. In this paper, we present an effective video classifier which employs the association rule mining technique to discover the actual dependence relationship between video states. The discriminatory state transition patterns mined from different video categories are then used to perform classification. Besides capturing the association between states in the time space, we also capture the association between low-level features in spatial dimension to further distinguish the semantics of videos. Experimental results show that the performance of our association rule based classifier is quite promising.

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cover image ACM Conferences
MMDB '04: Proceedings of the 2nd ACM international workshop on Multimedia databases
November 2004
118 pages
ISBN:1581139756
DOI:10.1145/1032604
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 13 November 2004

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Author Tags

  1. association rule
  2. frequent sequence
  3. video classification

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