Horror video scene recognition based on multi-view multi-instance learning

Authors:
Xinmiao Ding;Bing Li;Weiming Hu;Weihua Xiong;Zhenchong Wang
Affiliations:
China University of Mining and Technology, Beijing, China,National Laboratory of Pattern Recognition, Institute of Automation, CAS, China,Shandong Institute of Business and Technology, China;National Laboratory of Pattern Recognition, Institute of Automation, CAS, China;National Laboratory of Pattern Recognition, Institute of Automation, CAS, China;National Laboratory of Pattern Recognition, Institute of Automation, CAS, China;China University of Mining and Technology, Beijing, China
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Year:
2012

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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.