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Horror Video Scene Recognition Based on Multi-view Multi-instance Learning

  • Xinmiao Ding
  • Bing Li
  • Weiming Hu
  • Weihua Xiong
  • Zhenchong Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

Comparing with the research of pornographic content filtering on Web, Web horror content filtering, especially horror video scene recognition is still on the stage of exploration. Most existing methods identify horror scene only from independent frames, ignoring the context cues among frames in a video scene. In this paper, we propose a Multi-view Multi-Instance Leaning (M2IL) model based on joint sparse coding technique that takes the bag of instances from independent view and contextual view into account simultaneously and apply it on horror scene recognition. Experiments on a horror video dataset collected from internet demonstrate that our method’s performance is superior to the other existing algorithms.

Keywords

Sparse Code High Dimensional Feature Space Video Scene Scene Recognition Multiple Instance Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xinmiao Ding
    • 1
    • 2
    • 3
  • Bing Li
    • 2
  • Weiming Hu
    • 2
  • Weihua Xiong
    • 2
  • Zhenchong Wang
    • 1
  1. 1.China University of Mining and TechnologyBeijingChina
  2. 2.National Laboratory of Pattern Recognition, Institute of AutomationCASChina
  3. 3.Shandong Institute of Business and TechnologyChina

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