Detection of Shape Anomalies: A Probabilistic Approach Using Hidden Markov Models

  • Authors:

  • Zheng Liu;Jeffrey Xu Yu;Lei Chen; Di Wu

  • Affiliations:

  • The Chinese University of Hong Kong. zliu@se.cuhk.edu.hk;The Chinese University of Hong Kong. yu@se.cuhk.edu.hk;Hong Kong University of Science and Technology. leichen@cs.ust.hk;The Chinese University of Hong Kong. dwu@se.cuhk.edu.hk

  • Venue:
  • ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
  • Year:
  • 2008

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Abstract

We study the problem of detecting the shape anomalies in this paper. Our shape anomaly detection algorithm is performed on the one-dimensional representation (time series) of shapes, whose similarity is modeled by a generalized segmental hidden Markov model (HMM) under a scaling, translation and rotation invariant manner. Experimental results show that our proposed approach can find shape anomalies in a large collection of shapes effectively and efficiently.