Indexing Video Database for a CBVCD System

  • Debabrata DuttaEmail author
  • Sanjoy Kumar Saha
  • Bhabatosh Chanda
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)


In this work, we have presented a video database indexing methodology that works well for a content based video copy detection (CBVCD) system. Video data is first segmented into cohesive units called shots. A clustering based method is proposed to extract one or more Representative frames from the shots. On such collection of representatives extracted from all the shots in the video database, triangle inequality based image database indexing scheme is applied. Thus, video indexing is mapped to the task of image indexing. For a shot, following the proposed methodology primarily candidate shots corresponding to the matched representative frames are retrieved. Only on such small number of candidates the rigorous video sequence matching technique can be applied to make final decision by the CBVCD system or video retrieval system. Experimental result with a CBVCD system indicates significant gain in terms of speed, reduces false alarm rate without much compromise in terms of correct recognition rate in comparison to exhaustive search.


Video Database Indexing CBVCD 


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  1. 1.
    Brunelli, R., Mich, O., Moden, C.M.: A survey on the automatic indexing of video data. Journal of Visual Communication and Image Representation 10, 78–112 (1999)CrossRefGoogle Scholar
  2. 2.
    Smeaton, A.F.: Techniques used and open challenges to the analysis, indexing and retrieval of digital video. Information Systems 32, 545–559 (2007)CrossRefGoogle Scholar
  3. 3.
    Zhang, H.J., Wu, J., Zhong, D., Smoliar, S.W.: An integrated system for content based video retrieval and browsing. Pattern Recognition 30(4), 643–658 (1997)CrossRefGoogle Scholar
  4. 4.
    Bertini, M., Bimbo, A.D., Pala, P.: Indexing for reuse of tv news shot. Pattern Recognition 35, 581–591 (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Li, J.Z., OZsu, M.T., Szafron, D.: Modeling video temporal relationships in an object database systems. In: Proc. SPIE Multimedia Computing and Networking, pp. 80–91 (1997)Google Scholar
  6. 6.
    Pingali, G., Opalach, A., Jean, Y., Carlbom, I.: Instantly indexed multimedia databases of real world events. IEEE Trans. on Multimedia 4(2), 269–282 (2002)CrossRefGoogle Scholar
  7. 7.
    Ren, W., Singh, S., Singh, M., Zhu, Y.S.: State-of-the on spatio-temporal information-based video retrieval. Pattern Recognition 42 (2009)Google Scholar
  8. 8.
    Ren, W., Singh, S.: Video sequence matching with spatio-temporal constraint. In: Intl. Conf. Pattern Recog., pp. 834–837 (2004)Google Scholar
  9. 9.
    Fablet, R., Bouthmey, P.: Motion recognition using spatio-temporal random walks in sequence of 2d motion-related measurements. In: Proc. Intl. Conf. on Image Processing, pp. 652–655 (2001)Google Scholar
  10. 10.
    Fleuret, F., Berclaz, J., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. on PAMI 20(2), 267–282 (2008)CrossRefGoogle Scholar
  11. 11.
    Fu Ai, L., Qing Yu, J., Feng He, Y., Guan, T.: High-dimensional indexing technologies for large scale content-based image retrieval: A review. Journal of Zhejiang University-SCIENCE C (Computers & Electronics) 14(7), 505–520 (2013)CrossRefGoogle Scholar
  12. 12.
    Zhou, L.: Research on local features aggregating and indexing algorithm in large-scale image retrieval. Master Thesis, Huazhong University of Science and Technology, China 10–15 (2011)Google Scholar
  13. 13.
    Robinson, T.J.: The k-d-b tree: A search structure for large multidimensional dynamic indexes. In: Proc. ACM SIGMOD Intl. Conf. on Management of Data, pp. 10–18 (1981)Google Scholar
  14. 14.
    Skopal, T., Lokoc, J.: New dynamic construction techniques for m-tree. Journal of Discrete Algorithm 7(1), 62–77 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Lin, K.I., Jagadish, H.V., Faloutsos, C.: The tv-tree: An index structure for high-dimensional data. VLDB Journal 3(4), 517–542 (1994)CrossRefGoogle Scholar
  16. 16.
    Zhuang, Y., Liu, Y., Wu, F., Zhang, Y., Shao, J.: Hypergraph spectral hashing for similarity search of social image. In: Proc. ACM Int. Conf. on Multimedia, pp. 1457–1460 (2011)Google Scholar
  17. 17.
    Heo, J.P., Lee, Y., He, J., Chang, S.F., Yoon, S.E.: Spherical hashing. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2957–2964 (2012)Google Scholar
  18. 18.
    Avrithis, Y., Kalantidis, Y.: Approximate gaussian mixtures for large scale vocabularies. In: Proc. European Conf. on Computer Vision, pp. 15–28 (2012)Google Scholar
  19. 19.
    Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. PAMI 33(1), 117–128 (2011)CrossRefGoogle Scholar
  20. 20.
    Dutta, D., Saha, S.K., Chanda, B.: An attack invariant scheme for content-based video copy detection. Signal Image and Video Processing 7(4), 665–677 (2013)CrossRefGoogle Scholar
  21. 21.
    Mohanta, P.P., Saha, S.K., Chanda, B.: A model-based shot boundary detection technique using frame transition parameters. IEEE Trans. on Multimedia 14(1), 223–233 (2012)CrossRefGoogle Scholar
  22. 22.
    Berman, A.P., Shapiro, L.G.: A flexible image database system for content-based retrieval. Computer Vision and Image Understanding 75(1/2), 175–195 (1999)CrossRefGoogle Scholar
  23. 23.
    Ciocca, G., Schettini, R.: An innovative algorithm for key frame extraction in video summarization. Real Time IP 1, 69–98 (2006)Google Scholar
  24. 24.
    Mohanta, P.P., Saha, S.K., Chanda, B.: A novel technique for size constrained video storyboard generation using statistical run test and spanning tree. Int. J. Image Graphics 13(1) (2013)Google Scholar
  25. 25.
    Dunn, J.C.: Well separated clusters and optimal fuzzy partitions. Journal of Cybernetica 4, 95–104 (1974)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Debabrata Dutta
    • 1
    Email author
  • Sanjoy Kumar Saha
    • 2
  • Bhabatosh Chanda
    • 3
  1. 1.Tirthapati InstitutionKolkataIndia
  2. 2.Computer Science and Engineering DepartmentJadavpur UniversityKolkataIndia
  3. 3.ECS UnitIndian Statistical InstituteKolkataIndia

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