Coherent bag-of audio words model for efficient large-scale video copy detection

  • Authors:

  • Yang Liu;Wan-Lei Zhao;Chong-Wah Ngo;Chang-Sheng Xu;Han-Qing Lu

  • Affiliations:

  • Chinese Academy of Sciences, Beijing, China;City University of Hong Kong, Kowloon, Hong Kong;City University of Hong Kong, Kowloon, Hong Kong;Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China

  • Venue:
  • Proceedings of the ACM International Conference on Image and Video Retrieval
  • Year:
  • 2010

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Abstract

Current content-based video copy detection approaches mostly concentrate on the visual cues and neglect the audio information. In this paper, we attempt to tackle the video copy detection task resorting to audio information, which is equivalently important as well as visual information in multimedia processing. Firstly, inspired by bag-of visual words model, a bag-of audio words (BoA) representation is proposed to characterize each audio frame. Different from naive single-based modeling audio retrieval approaches, BoA is a high-level model due to its perceptual and semantical property. Within the BoA model, a coherency vocabulary indexing structure is adopted to achieve more efficient and effective indexing than single vocabulary of standard BoW model. The coherency vocabulary takes advantage of multiple audio features by computing co-occurrence of them across different feature spaces. By enforcing the tight coherency constraint across feature spaces, coherency vocabulary makes the BoA model more discriminative and robust to various audio transforms. 2D Hough transform is then applied to aggregate scores from matched audio segments. The segements fall into the peak bin is identified as the copy segments in reference video. In addition, we also accomplish video copy detection from both audio and visual cues by performing four late fusion strategies to demonstrate complementarity of audio and visual information in video copy detection. Intensive experiments are conducted on the large-scale dataset of TRECVID 2009 and competitve results are achieved.