A Multi-modal Clustering Method for Web Videos

  • Haiqi Huang
  • Yueming Lu
  • Fangwei Zhang
  • Songlin Sun
Part of the Communications in Computer and Information Science book series (CCIS, volume 320)


The prevalence of video sharing websites brings the explosion of web videos and poses a tough challenge to the web video clustering for their indexing. This paper proposes a flexible multi-modal clustering method for web videos. This method achieves web video representation and similarity measurement by integrating the extracted visual features, semantic features and text features of videos to describe a web video more accurately. With the multi-modal combined similarity as input, the affinity propagation algorithm is employed for the clustering procedure. The clustering method is evaluated by experiments conducted on web video dataset and has a better performance than existing methods.


multi-modal modified Hausdorff distance similarity fusion affinity propagation Algorithm average precision 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Haiqi Huang
    • 1
  • Yueming Lu
    • 1
    • 3
  • Fangwei Zhang
    • 2
  • Songlin Sun
    • 1
    • 3
  1. 1.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.School of HumanitiesBeijing University of Posts and TelecommunicationsBeijingChina
  3. 3.Key Laboratory of Trustworthy Distributed Computing and Service (BUPT)Ministry of EducationBeijingChina

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