Advertisement

A Tensor Framework for Data Stream Clustering and Compression

  • Bogusław CyganekEmail author
  • Michał Woźniak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10484)

Abstract

In the paper a tensor based method for video stream clustering and compression is presented. The method does video partitioning in temporal domain based on its content. Such coherent video partitions are amenable for better compression. The proposed method detects shot boundaries building a tensor model from a number of frames in the stream. To build the model, the best rank tensor decomposition is used. Each incoming tensor-frame is verified with the model based on the proposed concept drift detector – if it fits, then the model is updated with that frame. Otherwise, a model is rebuilt. This way obtained shots are then compressed also with the best rank tensor decomposition methods.

Keywords

Video shot detection Signal compression Tensor-frames Best-rank tensor decomposition Stream tensor analysis 

Notes

Acknowledgement

This work was supported by the National Science Centre, Poland, under the grant no. 2016/21/B/ST6/01461.

References

  1. 1.
    Asghar, M.N., Hussain, F., Manton, R.: Video indexing: a survey. Int. J. Comput. Inf. Technol. 03(01), 148–169 (2014)Google Scholar
  2. 2.
    de Avila, S.E.F., Lopes, A.P.B., da Luz Jr., A., Araújo, A.A.: VSUMM: a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recogn. Lett. 32, 56–68 (2011)CrossRefGoogle Scholar
  3. 3.
    Cyganek, B.: An analysis of the road signs classification based on the higher-order singular value decomposition of the deformable pattern tensors. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2010. LNCS, vol. 6475, pp. 191–202. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-17691-3_18 CrossRefGoogle Scholar
  4. 4.
    Cyganek, B.: Object Detection and Recognition in Digital Images. Theory and Practice. Wiley, Hoboken (2013)Google Scholar
  5. 5.
    Cyganek, B., Woźniak, M.: On robust computation of tensor classifiers based on the higher-order singular value decomposition. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds.) Software Engineering Perspectives and Application in Intelligent Systems. Advances in Intelligent Systems and Computing, vol. 465, pp. 193–201. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-33622-0_18
  6. 6.
    Cyganek, B., Woźniak, M.: Tensor Based Shot Boundary Detection in Video Streams. Springer, Submitted to the New Generation Computing (2017)Google Scholar
  7. 7.
  8. 8.
    Del Fabro, M., Böszörmenyi, L.: State-of-the-art and future challenges in video scene detection: a survey. Multimedia Syst. 19(5), 427–454 (2013). SpringerCrossRefGoogle Scholar
  9. 9.
    Furini, M., Geraci, F., Montangero, M., Pellegrini, M.: STIMO: STIll and moving video storyboard for the web scenario. Multimedia Tools Appl. 46(1), 47–69 (2010)CrossRefGoogle Scholar
  10. 10.
    Gama, J.: Knowledge Discovery from Data Streams. CRC Press, Boca Raton (2010)Google Scholar
  11. 11.
  12. 12.
    Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Kuanar, S.K.: Video key frame extraction through dynamic Delaunay clustering with a structural constraint. J. Vis. Commun. Image Represent. V24(7), 1212–1227 (2013)CrossRefGoogle Scholar
  14. 14.
    de Lathauwer, L., de Moor, B., Vandewalle, J.: On the best Rank-1 and Rank-(R1, R2, …, RN) approximation of higher-order tensors. SIAM J. Matrix Anal. Appl. 21(4), 1324–1342 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Mahmoud, K.M., Ismail, M.A., Ghanem, N.M.: VSCAN: an enhanced video summarization using density-based spatial clustering. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8156, pp. 733–742. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41181-6_74 CrossRefGoogle Scholar
  16. 16.
    DeMenthon, D., Kobla, V., Doermann, D.: Video summarization by curve simplification. In: Proceedings of the Sixth ACM International Conference on Multimedia, pp. 211–218. ACM (1998)Google Scholar
  17. 17.
    Mundur, P., Rao, Y., Yesha, Y.: Keyframe-based video summarization using Delaunay clustering. Int. J. Dig. Libr. 6(2), 219–232 (2006)CrossRefGoogle Scholar
  18. 18.
    Sun, J., Tao, D., Faloutsos, C.: Beyond streams and graphs: dynamic tensor analysis. In: KDD 2006, Philadelphia, Pennsylvania, USA (2006)Google Scholar
  19. 19.
    Sun, J., Tao, D., Faloutsos, C.: Incremental tensor analysis: theory and applications. ACM Trans. Knowl. Discovery Data 2(3), 11:1–11:37 (2008)CrossRefGoogle Scholar
  20. 20.
    Truong, B.T., Venkatesh, S.: Video abstraction: a systematic review and classification. ACM Trans. Multimedia Comput. Comm. Appl. 3(1) (2007)Google Scholar
  21. 21.
    Tucker, L.R.: Some mathematical notes on three-mode factor analysis. Psychometrika 31, 279–311 (1966)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Valdes, V., Martinez, J.: Efficient video summarization and retrieval tools. In: International Workshop on Content-Based Multimedia Indexing, pp. 43–48 (2011)Google Scholar
  23. 23.
    Wang, H., Ahuja, N.: Compact representation of multidimensional data using tensor rank-one decomposition. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 1, pp. 44–47 (2004)Google Scholar
  24. 24.
    Wang, H., Ahuja, N.: A tensor approximation approach to dimensionality reduction. Int. J. Comput. Vis. 76(3), 217–229 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakówPoland
  2. 2.Wrocław University of Science and TechnologyWrocławPoland

Personalised recommendations