Skip to main content
SpringerLink
Log in

VIDCAR: an unsupervised CBVR framework for identifying similar videos with prominent object motion

  • Regular Paper
  • Published:
International Journal of Multimedia Information Retrieval Aims and scope Submit manuscript

Abstract

This paper presents VIDeo Content Analysis and Retrieval (VIDCAR), an unsupervised framework for Content-Based Video Retrieval (CBVR) using representation of the dynamics in the spatio-temporal model extracted from video shots. We propose Dynamic Multi Spectro Temporal-Curvature Scale Space (DMST-CSS), an improved feature descriptor for enhancing the performance of CBVR task. Our primary contribution is in representation of the dynamics of the evolution of the MST-CSS surface. Unlike the earlier MST-CSS descriptor [22], which extracts geometric features after the evolving MST-CSS surface converges to a final formation, this DMST-CSS captures the dynamics of the evolution (formation) of the surface and is thus more robust. We have represented the dynamics of MST-CSS surface as a multivariate time series to obtain a DMST-CSS descriptor. A global kernel alignment technique has been adapted to compute a match cost between query and model DMST-CSS descriptor. In our experiments, VIDCAR was shown to have greater precision recall than the competitors on five datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Aggarwal G, Chowdhury A, Chellappa R (2004) A system identification approach for video-based face recognition. In: ICPR, pp 175–178

  2. Auguste R, El Ghini A, Bilasco M, Ihaddadene N, Djeraba C (2010) Motion similarity measure between video sequences using multivariate time series modeling. In: ICMWI, pp 292–296

  3. Babu RV, Ramakrishnan KR (2007) Compressed domain video retrieval using object and global motion descriptors. MTA 32(1):93–113

  4. Barnich O, Droogenbroeck MV (2011) ViBe: a universal background subtraction algorithm for video sequences. IEEE TIP 20(6):1709–1724

  5. Basharat A, Zhai Y, Shah M (2008) Content based video matching using spatiotemporal volumes. CVIU 110(3):360–377

  6. Bashir FI, Member S, Khokhar AA, Member S, Schonfeld D, Member S (2007) Real-time motion trajectory-based indexing and retrieval of video sequences. IEEE TM 9:58–65

    Google Scholar 

  7. Bissacco A, Chiuso A, Ma Y, Soatto S (2001) Recognition of human gaits. CVPR 2:52–57

    Google Scholar 

  8. Brendel W, Todorovic S (2010) Activities as time series of human postures. In: ECCV, pp 721–734

  9. Chattopadhyay C, Das S (2013) STAR: a content based video retrieval system for moving camera video shots. In: NCVPRIPG, pp 1–4

  10. Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. IJCV 22(1):61–79

    MATH  Google Scholar 

  11. Chattopadhyay C, Das S (2012) A novel hyperstring based descriptor for an improved representation of motion trajectory and retrieval of similar video shots with static camera. In: EAIT, pp 174–177

  12. Chattopadhyay C, Das S (2012) Enhancing the MST-CSS representation using robust geometric features, for efficient content based video retrieval (CBVR). In: ISM, pp 352–355

  13. Chattopadhyay C, Maurya AK (2013) Multivariate time series modeling of geometric features of spatio-temporal volumes for content based video retrieval. IJMIR 3:15–28

  14. Chellappa R, Sankaranarayanan AC, Veeraraghavan A, Turaga P (2010) Statistical methods and models for video-based tracking, modeling, and recognition. Found Trends Signal Process 3:1–151

    Article  Google Scholar 

  15. Chen PY, Chen ALP (2003) Video retrieval based on video motion tracks of moving objects. Proc SPIE 5307:550–558

    Article  Google Scholar 

  16. Chorley RJ, Morley LSD (1959) A simplified approximation for the hypsometric integral. J Geol, pp 566–571

  17. Cui B, Zhao Z, Tok WH (2012) A framework for similarity search of time series cliques with natural relations. IEEE TKDE 24(3):385–398

    Google Scholar 

  18. Cuturi M (2011) Fast global alignment kernels. In: ICML, pp 929–936

  19. Deng Y, Mukherjee D, Manjunath BS (1998) NeTra-V: toward an object-based video representation. IEEE TCSVT 8:616–627

    Google Scholar 

  20. Doretto G, Chiuso A, Wu YN, Soatto S (2003) Dynamic textures. IJCV 51(2):91–109

  21. Dyana A, Das S (2009) Trajectory representation using Gabor features for motion-based video retrieval. Pattern Recogn Lett 30(10):877–892

  22. Dyana A, Das S (2010) MST-CSS (Multi-Spectro-Temporal Curvature Scale Space), a novel spatio-temporal representation for content-based video retrieval. IEEE TCSVT 20(8):1080–1094

  23. Elliot JK (1989) An investigation of the change in surface roughness through time on the foreland of austre okstindbreen, North Norway. Comput Geosci 15:209–217

    Article  Google Scholar 

  24. Erol B, Kossentini F (2005) Shape-based retrieval of video objects. IEEE TM 7(1):179–182

  25. Fiedler M (1973) Algebraic connectivity of graphs. Czechoslov Math J 23(98):298–305

    MathSciNet  Google Scholar 

  26. Florez OU, Lim S (2009) Discovery of time series in video data through distribution of spatiotemporal gradients. In: SAC, pp 1816–1820

  27. Gao HP, Yang ZQ (2010) Content based video retrieval using spatiotemporal salient objects. In: IPTC, pp 689–692

  28. Ghosh A, Boyd S (2006) Upper bounds on algebraic connectivity via convex optimization. Linear Algebra Appl

  29. Giga Y (2006) Surface evolution equations: a level set approach, 1st edn. Springer

  30. Gorelick L, Blank M, Shechtman E, Irani M, Basri R (2007) Actions as space-time shapes. IEEE TPAMI 29(12):2247–2253

  31. Hou S, Zhou S, Siddique M (2013) A compressed sensing approach for query by example video retrieval. MTA 72(3):3031–3044

  32. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. IEEE TPAMI 34(7):1409–1422

    Article  Google Scholar 

  33. Kolmogorov V, Zabih R (2004) What energy functions can be minimized via graph cuts. IEEE TPAMI 26:65–81

    Article  Google Scholar 

  34. Laxman S, Sastry P (2006) A survey of temporal data mining. Sadhana 31(2):173–198

  35. Lee SL, Chun SJ, Kim DH, Lee JH, Chung CW (2000) Similarity search for multidimensional data sequences. In: ICDE, pp 599–608

  36. Liang B, Xiao W, Liu X (2012) Design of video retrieval system using MPEG-7 descriptors. In: Procedia engineering, pp 2578–2582

  37. Lin J, Li Y (2009) Finding structural similarity in time series data using bag-of-patterns representation. In: SSDBM, pp 461–477

  38. Ma Y, Zhang H (2002) Motion texture: a new motion based video representation. In: ICPR, pp 548–551

  39. Madokoro H, Tsukada M, Sato K (2013) Unsupervised and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environment. Pattern Recogn Phys 1(1):63–74

    Article  Google Scholar 

  40. O’Neill B (1997) Elementary differential geometry, 2nd edn. Academic Press

  41. Peyre G (2011) The numerical tours of signal processing. Comput Sci Eng 13(4):94–97

    Article  Google Scholar 

  42. Popivanov I, Miller RJ (2002) Similarity search over time series data using wavelets. In: ICDE, pp 212–221

  43. Poullot S, Buisson O, Crucianu M (2010) Scaling content-based video copy detection to very large databases. Multimed Tools Appl 47(2):279–306

    Article  Google Scholar 

  44. Reddy KK, Shah M (2012) Recognizing 50 human action categories of web videos. MVA 24(5):971–981

  45. Richard JC, Morley LSD (2005) Measurement of DEM roughness using the local fractal dimension. Geomorphologie, pp 327–338

  46. Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In: ICPR, pp 32–36

  47. Sellier D, Plank MJ, Harrington JJ (2011) A mathematical framework for modelling cambial surface evolution using a level set method. An Bot 108:1001–1011

    Article  Google Scholar 

  48. Singh O, Sarangi A, Sharma C (2008) Hypsometric integral estimation methods and its relevance on erosion status of north-western lesser himalayan watersheds. Water Resour Manag, pp. 1545–1560

  49. Turaga PK, Veeraraghavan A, Srivastava A, Chellappa R (2011) Statistical computations on Grassmann and Stiefel manifolds for image and video-based recognition. IEEE TPAMI 33(11):2273–2286

  50. Vaduva C, Costachioiu T, Patrascu C, Gavat I, Lazarescu V, Datcu M (2013) A latent analysis of earth surface dynamic evolution using change map time series. IEEE TGRS 51(4):2105–2118

    Google Scholar 

  51. Yang C, Zhang L, Lu H, Ruan X, Yang MH (2013) Saliency detection via graph-based manifold ranking. In: CVPR

  52. Yilmaz A, Shah M (2008) A differential geometric approach to representing the human actions. CVIU 109(3):335–351

  53. Zhang D, Zuo W, Zhang D, Zhang H (2010) Time series classification using support vector machine with Gaussian elastic metric kernel. In: ICPR, pp 29–32

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology Madras, Chennai, 600036, India

    Chiranjoy Chattopadhyay

Authors
  1. Chiranjoy Chattopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chiranjoy Chattopadhyay.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chattopadhyay, C. VIDCAR: an unsupervised CBVR framework for identifying similar videos with prominent object motion. Int J Multimed Info Retr 4, 59–72 (2015). https://doi.org/10.1007/s13735-014-0070-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13735-014-0070-z

Keywords

Search

Navigation