A generic framework for event detection in various video domains

  • Authors:

  • Tianzhu Zhang;Changsheng Xu;Guangyu Zhu;Si Liu;Hanqing Lu

  • Affiliations:

  • Institute of Automation, Chinese Academy of Sciences , Beijing, China;Institute of Automation, Chinese Academy of Sciences , Beijing, China;National University of Singapore, Singapore, Singapore;Institute of Automation, Chinese Academy of Sciences , Beijing, China;Institute of Automation, Chinese Academy of Sciences , Beijing, China

  • Venue:
  • Proceedings of the international conference on Multimedia
  • Year:
  • 2010

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Abstract

Event detection is essential for the extensively studied video analysis and understanding area. Although various approaches have been proposed for event detection, there is a lack of a generic event detection framework that can be applied to various video domains (e.g. sports, news, movies, surveillance). In this paper, we present a generic event detection approach based on semi-supervised learning and Internet vision. Concretely, a Graph-based Semi-Supervised Multiple Instance Learning (GSSMIL) algorithm is proposed to jointly explore small-scale expert labeled videos and large-scale unlabeled videos to train the event models to detect video event boundaries. The expert labeled videos are obtained from the analysis and alignment of well-structured video related text (e.g. movie scripts, web-casting text, close caption). The unlabeled data are obtained by querying related events from the video search engine (e.g. YouTube) in order to give more distributive information for event modeling. A critical issue of GSSMIL in constructing a graph is the weight assignment, where the weight of an edge specifies the similarity between two data points. To tackle this problem, we propose a novel Multiple Instance Learning Induced Similarity (MILIS) measure by learning instance sensitive classifiers. We perform the thorough experiments in three popular video domains: movies, sports and news. The results compared with the state-of-the-arts are promising and demonstrate our proposed approach is performance-effective.