Video Shot Extraction on Parallel Architectures

  • Pablo Toharia
  • Oscar D. Robles
  • José L. Bosque
  • Angel Rodríguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4330)


One of the main objectives of Content-based Multimedia Retrieval systems is the automation of the information extraction process from raw data. When dealing with video data, the first step is to perform a temporal video segmentation in order to make a shot decomposition of the video content. From a computational point of view, this is a very high demanding task and algorithm optimization must be seeked. This paper presents a comparison between two different parallel programming paradigms: shared-memory communication and distributed memory processing using the message passing paradigm. Taking into account the software solutions, experimental results are collected over two alternative parallel architectures: a shared-memory symmetric multiprocessor and a cluster. This paper analyzes the performance achieved from the viewpoints of speed and scalability.


Parallel Architecture Zernike Moment Package Size Video Shot Shot Boundary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    del Bimbo, A.: Visual Information Retrieval. Morgan Kaufmann Publishers, San Francisco (1999)Google Scholar
  2. 2.
    Marques, O., Furht, B.: Content-based Image and Video Retrieval. Kluwer Academic Publishers, Dordrecht (2002)MATHGoogle Scholar
  3. 3.
    Petkovic, M., Jonker, W.: Content-Based Video Retrieval. Springer, Heidelberg (2003)Google Scholar
  4. 4.
    Shih, T.K. (ed.): Distributed multimedia databases: techniques & applications. Idea Group Publishing, USA (2002)Google Scholar
  5. 5.
    Srakaew, S., Alexandridis, N., Nga, P.P., Blankenship, G.: Content-based multimedia data retrieval on cluster system environment. In: Sloot, et al. (eds.) 7th International Conference HPCN Europe 1999, pp. 1235–1241. Springer, Heidelberg (1999)Google Scholar
  6. 6.
    Kao, O., Steinert, G., Drews, F.: Scheduling aspects for image retrieval in cluster-based image databases. In: Buyya, et al. (eds.) Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 329–336. IEEE Comp. Soc., Los Alamitos (2001)CrossRefGoogle Scholar
  7. 7.
    Bosque, J.L., Robles, O.D., Pastor, L., Rodríguez, A.: Performance analysis of a CBIR system on shared-memory systems and heterogeneous clusters. In: Proceedings on IEEE CAMP 2005, pp. 309–314. IEEE, Los Alamitos (2005)Google Scholar
  8. 8.
    Krishnamurthy, E.: Parallel Processing: Principles and Practice. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  9. 9.
    Hwang, K.: Advanced Computer Architecture: Parallelism, Scalability, Programmability. McGraw-Hill, New York (1993)Google Scholar
  10. 10.
    Pitas, I. (ed.): Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks. John Wiley & Sons, Chichester (1993)MATHGoogle Scholar
  11. 11.
    Nupairoj, N., Ni, L.M.: Performance evaluation of some MPI implementations on workstations clusters. In: Proceedings of the SPLC 1994, pp. 98–105 (1994)Google Scholar
  12. 12.
    Bruck, J., Dolev, D., Ho, C.T., Rosu, M., Strong, R.: Efficient message passing interface MPI for parallel computing on clusters of workstations. Journal of Parallel and Distributed Computing 40, 19–34 (1997)CrossRefGoogle Scholar
  13. 13.
    The beowulf cluster site. Web (2006), (retrieved september 15, 2006),
  14. 14.
    Porter, S., Mirmehdi, M., Thomas, B.: Temporal video segmentation and classification of edit effects. Image and Vision Computing 21(13–14), 1097–1106 (2003)CrossRefGoogle Scholar
  15. 15.
    Valencia, G., Rodríguez, J.A., Urdiales, C., Sandoval, F.: Color-based video segmentation using interlinked irregular pyramids. Pattern Recognition 37(2), 377–380 (2004)MATHCrossRefGoogle Scholar
  16. 16.
    Antani, S., Kasturi, R., Jain, R.: A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern Recognition 35(4), 945–965 (2002)MATHCrossRefGoogle Scholar
  17. 17.
    Robles, O.D., Toharia, P., Rodríguez, A., Pastor, L.: Towards a content-based video retrieval system using wavelet-based signatures. In: Hamza, M.H. (ed.) 7th IASTED International Conference on CGIM 2004, IASTED, pp. 344–349. ACTA Press (2004)Google Scholar
  18. 18.
    Černeková, Z., Nikou, C., Pitas, I.: Shot detection in video sequences using entropy-based metrics. In: Proc. of the IST 2001, Supported by the MOUMIR project, Iran, pp. 156–165 (2001)Google Scholar
  19. 19.
    Zhang, D., Qi, W., Zhang, H.J.: A new shot boundary detection algorithm. In: Shum, H.-Y., Liao, M., Chang, S.-F. (eds.) PCM 2001. LNCS, vol. 2195, pp. 63–70. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  20. 20.
    Khotanzad, A., Hong, Y.H.: Invariant image recognition by zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(5), 489–497 (1990)CrossRefGoogle Scholar
  21. 21.
    Kamila, N.K., Mahapatra, S., Nanda, S.: Invariance image analysis using modified Zernike moments. Pattern Recognition Letters 26(6), 747–753 (2005)CrossRefGoogle Scholar
  22. 22.
    Toharia, P., Robles, O.D., Rodríguez, Á., Pastor, L.: Combining shape and color for automatic video cut detection. In: Proc. of the TRECVID 2005 Workshop, pp. 336–345 (2005)Google Scholar
  23. 23.
    Toharia, P., Robles, O.D., Rodríguez, A., Pastor, L.: Xml specification for avi files in a content-based video retrieval system. In: Villanueva, J.J. (ed.) Proceedings of the Fourth IASTED International Conference on VIIP, IASTED, pp. 374–378. ACTA Press (2004)Google Scholar
  24. 24.
    Bilas, A., Fritts, J., Singh, J.P.: Real-time parallel mpeg-2 decoding in software. In: IPPS 1997, pp. 197–203. IEEE Computer Society, Los Alamitos (1997)Google Scholar
  25. 25.
    Bhandarkar, S.M., Chandrasekaran, S.R.: Parallel parsing of mpeg video on a shared-memory symmetric multiprocessor. Parallel Computing 30(11), 1233–1276 (2004)CrossRefGoogle Scholar
  26. 26.
    Leroy, X.: The linuxthreads library. Web (2006),
  27. 27.
    MPI Forum: A Message-Passing Interface standard. Web (2006) (retrieved september 15, 2006),
  28. 28.
    Argonne National Laboratory: Mpich vs. (2004), Web (2006), (retrieved september 15, 2006),
  29. 29.
    Squyres, J.M., Meyer, K.L., McNally, M.D., Lumsdaine, A.: LAM/MPI User Guide. University of Notre Dame, LAM 6.3 (1998)Google Scholar
  30. 30.
    Universidad Politécnica de Madrid (UPM) and Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT): Centro de supercomputación y visualización de madrid. Web (2006) (retrieved september 15, 2006),
  31. 31.
    Standard Performance Evaluation Corporation: Spec benchmarks. Web (2006), (retrieved september 15, 2006) from source,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pablo Toharia
    • 1
  • Oscar D. Robles
    • 1
  • José L. Bosque
    • 1
    • 2
  • Angel Rodríguez
    • 3
  1. 1.Dept. de Arquitectura y Tecnología de Computadores e Inteligencia ArtificialUniversidad Rey Juan Carlos (URJC) 
  2. 2.Dpto. de Electrónica y ComputadoresUniversidad de Cantabria (UC) 
  3. 3.Dept. de Tecnología FotónicaUniversidad Politécnica de Madrid (UPM) 

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