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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)

Abstract

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.

Keywords

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

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References

  1. 1.
  2. 2.
    Brezeale, D., Cook, D.J.: Automatic Video Classification: A Survey of the Literature. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 38(3) (2008)Google Scholar
  3. 3.
    Liu, S., Zhu, M., Zheng, Q.: Mining Similarities for Clustering Web Video Clips. In: International Conference on Computer Science and Software Engineering, pp. 759–762 (2008)Google Scholar
  4. 4.
    Jain, A.K.: Data Clustering: 50 Years Beyond K-means. Pattern Recognition Letters 31(8), 651–666 (2010)CrossRefGoogle Scholar
  5. 5.
    Juasiripukdee, P., Wiyartanti, L., Kim, L.: Clustering Search Results of Non-text User Generated Content. In: ICDIM 2010 (2010)Google Scholar
  6. 6.
    Rasiwasia, N.J., Pereira, C., Coviello, E., Doyle, G., Lanckriet, G.R., Levy, R., Vasconcelos, N.: A New Approach to Cross-modal Multimedia Retrieval. In: ACM Multimedia, pp. 251–260 (2010)Google Scholar
  7. 7.
    Wei, S., Zhao, Y., Zhu, Z., Liu, N.: Multimodal Fusion for Video Search Reranking. IEEE Transactions on Knowledge and Data Engineering 99(1), 1191–1199 (2009)Google Scholar
  8. 8.
    Yang, L., Liu, J., Yang, X., Hua, X.S.: Multi-modality Web Video Categorization. In: Multimedia Information Retrieval (MIR), pp. 265–274 (2007)Google Scholar
  9. 9.
    Hindle, A., Shao, J., Lin, D., Lu, J., Zhang, R.: Clustering Web Video Search Results Based on Integration of Multiple Features. World Wide Web 14(1), 1–21 (2010)Google Scholar
  10. 10.
    Lowe, D.G.: Distinctive Image Features from Scale-invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Frey, B.J., Dueck, D.: Clustering by Passing Messages Between Data Points. Science 315(5814), 972–976 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Cao, J., Zhang, Y.D., Song, Y.C., Chen, Z.N., Zhang, X., Li, J.T.: MCG-WEBV: A Benchmark Dataset for Web Video Analysis. Technical Report, MCG-ICT-CAS-09-001, Institute of Computing Technology (2009)Google Scholar
  13. 13.
    Kishida, K.: Property of Average Precision and Its Generalization: An Examination of Evaluation Indicator for Information Retrieval Experiments. Nii technical report (2005)Google Scholar

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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