Multimedia Tools and Applications

, Volume 66, Issue 3, pp 545–572 | Cite as

Video content categorization using the double decomposition

  • Youtian Du
  • Feng Chen
  • Wenli Xu
  • Xueming Qian


Video contents contain complex structures due to the variety of the components and events involved. For example, surveillance videos often record multi-object interactions and consist of various scales of motion detail; Web videos are composed of multimodal cues, and each cue generally consists of a variety of scales of information. Generally, video contents comprise two types of the combination of the inherent structures: multi-modality/multi-scale and multi-object /multi-scale. Therefore, in this paper, we propose a new framework for video content modeling, under which video contents are decomposed into multiple interacting processes by double decomposition that aims at each type of combination of structures. To model the resulting processes, we propose a method named double-decomposed hidden Markov models (DDHMMs). DDHMMs contain multiple state chains that correspond to the interacting processes. To make the switching frequency of states in each chain consistent with the scale of the corresponding process, a durational state variable is introduced in DDHMMs. The proposed method performs well in modeling the relations among the interacting processes and the dynamics of each. We discuss the appropriate features under the proposed framework and evaluate DDHMMs in two applications, human motion recognition and web video categorization. The experimental results demonstrate that the double decomposition enhances video categorization performance in both cases.


Video content categorization Double decomposition Dynamic Bayesian network Multiple scales Stochastic process 



The research presented in this paper is supported in part by the National Natural Science Foundation (60905018, 60903121, 61173109, 61175039), Key Projects in the National Science & Technology Pillar Program (2011BAK08B02), Research Fund for Doctoral Program of Higher Education (20090201120032), Fundamental Research Funds for the Central Universities (xjj2009041, xjj20100051), of China. The authors would like to thank the video team at United Technologies Research Center (UTRC) for their pertinent and constructive discussion, and thank Dr. K.P. Murphy for his Matlab Bnet toolbox. Also, the authors would like to thank all the anonymous reviewers for their constructive advices.


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© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Ministry of Education Key Lab for Intelligent Networks and Network SecurityXi’an Jiaotong UniversityXi’anChina
  2. 2.Department of AutomationTsinghua UniversityBeijingChina
  3. 3.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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