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Studying the impact of sequence clustering on near-duplicate video retrieval: an experimental comparison

  • Yandan Wang
  • Mohammed BelkhatirEmail author
Regular Paper

Abstract

In this paper, we propose studying the impact of clustering on near-duplicate video (NDV) retrieval. The aim is to reduce the search space at retrieval time through a pre-processing clustering step performed on the dataset off-line and retrieving NDVs based on the formed clusters. Our contribution is a novel clustering framework inspired by a bioinformatics technique, namely DNA multiple sequence alignment (MSA). A series of video keyframes in chronological order is represented as an alphabetical genome, analogous to a DNA sequence and MSA is employed to automatically partition the NDVs in a video collection into clusters. After discussing the advantages and shortcomings of the main state-of-the-art clustering approaches for video clustering in the theoretical part of the paper, we empirically evaluate the performance of the proposed MSA-based framework against five clustering algorithms representative of these mainstream approaches: Birch, Cure, Dbscan, Expectation-Maximization and Proclus. Also, we show that our clustering-based approach, while being significantly faster than non-clustering-based n-gram and edit distance NDV retrieval techniques, yields better mean average precision retrieval accuracy.

Keywords

Near-duplicate video retrieval Clustering Multiple sequence alignment 

References

  1. 1.
    Belkhatir M, Tahayna B (2012) Near-duplicate video detection featuring coupled temporal and perceptual visual structures and logical inference based matching. Inf Process Manage 48(3):489–501Google Scholar
  2. 2.
    Notredame C, Higgins DG, Heringa J (2000) T-coffee: a novel method for fast and accurate multiple sequence alignment. J Mol Biol 302(1):205–217CrossRefGoogle Scholar
  3. 3.
    Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32(5):1792–1797Google Scholar
  4. 4.
    MacQueen J et al (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1, pp 281–297Google Scholar
  5. 5.
    Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22(22):4673–4680Google Scholar
  6. 6.
    Wu X, Ngo CW, Hauptmann AG, Tan HK (2009) Real-time near-duplicate elimination for web video search with content and context. IEEE Trans Multimedia 11(2):196–207CrossRefGoogle Scholar
  7. 7.
    Yuan J, Tian Q, Ranganath S (2004) Fast and robust search method for short video clips from large video collection. In: Proceedings of the 17th international conference on pattern recognition, ICPR, vol 4, pp 866–869Google Scholar
  8. 8.
    Feng DF, Doolittle RF (1987) Progressive sequence alignment as a prerequisitet to correct phylogenetic trees. J Mol Evol 25(4):351–360CrossRefGoogle Scholar
  9. 9.
    Song J, Yang Y, Huang Z, Shen HT, Hong R (2011) Multiple feature hashing for real-time large scale near-duplicate video retrieval. In: Proceedings of the 19th ACM international conference on Multimedia, pp 423–432Google Scholar
  10. 10.
    Shang L, Yang L, Wang F, Chan KP, Hua XS (2010) Real-time large scale near-duplicate web video retrieval. In: Proceedings of 18th ACM international conference on Multimedia, pp 531–540Google Scholar
  11. 11.
    Cai Y, Yang L, Ping W, Wang F, Mei T, Hua XS, Li S (2011) Million-scale near-duplicate video retrieval system. In: Proceedings of the 19th ACM international conference on multimedia, pp 837–838Google Scholar
  12. 12.
    Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. ACM SIGMOD Record 25:103–114CrossRefGoogle Scholar
  13. 13.
    Guha S, Rastogi R, Shim K (1998) CURE: an efficient clustering algorithm for large databases. ACM SIGMOD Record 27:73–84Google Scholar
  14. 14.
    McLachlan GJ, Krishnan T (1997) The EM algorithm and extensions, vol 274. Wiley, New YorkGoogle Scholar
  15. 15.
    Aggarwal CC, Wolf JL, Yu PS, Procopiuc C, Park JS (1999) Fast algorithms for projected clustering. ACM SIGMOD Record 28(2):61–72CrossRefGoogle Scholar
  16. 16.
    Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining, vol 1996, pp 226–231Google Scholar
  17. 17.
    Shen HT, Zhou X, Huang Z, Shao J, Zhou X (2007) UQLIPS: a real-time near-duplicate video clip detection system. In: Proceedings of the 33rd international conference on very large data bases, pp 1374–1377Google Scholar
  18. 18.
    Tan HK, Ngo CW, Hong R, Chua TS (2009) Scalable detection of partial near-duplicate videos by visual-temporal consistency. In: Proceedings of the 17th ACM international conference on multimedia, pp 145–154Google Scholar
  19. 19.
    Paisitkriangkrai S, Mei T, Zhang J, Hua XS (2010) Scalable clip-based near-duplicate video detection with ordinal measure. In: Proceedings of the ACM international conference on image and video retrieval, pp 121–128Google Scholar
  20. 20.
    Duan LY, Yuan JS, Tian Q, Xu CS (2004) Fast and robust video clip search using index structure. In: Proceedings of the 12th annual ACM international conference on multimedia, pp 756–757Google Scholar
  21. 21.
    Gao L, Li Z, Katsaggelos AK (2008) A kd-tree based dynamic indexing scheme for video retrieval and geometry matching. In: Proceedings of 17th international conference on computer communications and networks, ICCCN’08, pp 1–5Google Scholar
  22. 22.
    Chatterjee K, Chen SC (2008) GeM-tree: towards a generalized multidimensional index structure supporting image and video retrieval. In: Proceedings of the 10th IEEE international symposium on multimedia, ISM 2008, pp 631–636Google Scholar
  23. 23.
    Aggarwal CC, Yu PS (2000) Finding generalized projected clusters in high dimensional spaces. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 70–81Google Scholar
  24. 24.
    Lu G (2002) Techniques and data structures for efficient multimedia retrieval based on similarity. IEEE Trans Multimedia 4(3):372–384CrossRefGoogle Scholar
  25. 25.
    Hua XS, Chen X, Zhang HJ (2004) Robust video signature based on ordinal measure. In: Proceedings of the international conference on image processing, ICIP’04, vol 1, pp 685–688Google Scholar
  26. 26.
    Zhu J, Hoi SC, Lyu MR, Yan S (2008) Near-duplicate keyframe retrieval by nonrigid image matching. In: Proceedings of the 16th ACM international conference on multimedia, pp 41–50Google Scholar
  27. 27.
    Ke Y, Sukthankar R, Huston L (2004) Efficient near-duplicate detection and sub-image retrieval. In: Proceedings of the 12th annual ACM international conference on multimedia, pp 869–876Google Scholar
  28. 28.
    Zhou J, Zhang XP (2005) Automatic identification of digital video based on shot-level sequence matching. In: Proceedings of the 13th annual ACM international conference on multimedia, pp 515–518Google Scholar
  29. 29.
    Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 94–105Google Scholar
  30. 30.
    Cheng X, Chia LT (2010) Stratification-based keyframe cliques for removal of near-duplicates in video search results. In: Proceedings of the international conference on multimedia information retrieval, pp 313–322Google Scholar
  31. 31.
    Zhou X, Chen L (2010) Monitoring near duplicates over video streams. In: Proceedings of the 18th ACM international conference on multimedia, pp 521–530Google Scholar
  32. 32.
    Tian X, Yang L, Wang J, Yang Y, Wu X, Hua XS (2008) Bayesian video search reranking. In: Proceeding of the 16th ACM international conference on multimedia, pp 131–140Google Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.School of Physics and Electronic Information EngineeringWenzhou UniversityWenzhouChina
  2. 2.Faculty of Computer ScienceUniversity of LyonVilleurbanneFrance

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