Skip to main content
SpringerLink
Log in

Adaptive Video Transition Detection Based on Multiscale Structural Dissimilarity

  • Conference paper
  • First Online:
Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

Included in the following conference series:

Abstract

The fast growth in the acquisition and dissemination of videos has driven the development of diverse multimedia applications, such as interactive broadcasting, entertainment, surveillance, telemedicine, among others. Due to the massive amount of generated data, a challenging task is to store, browse and retrieve video content efficiently. This work describes and analyzes a novel automatic video transition method based on multiscale inter-frame dissimilarity vectors. The shot frames are identified by means of an adaptive local threshold mechanism. Experimental results demonstrate that the proposed approach is capable of achieving high accuracy rates when applied to several video sequences.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Huang, T.S.: Image Sequence Analysis, vol. 5. Springer Science & Business Media, Berlin (2013)

    Google Scholar 

  2. Koprinska, I., Carrato, S.: Temporal video segmentation: a survey. Sig. Process. Image Commun. 16, 477–500 (2001)

    Article  Google Scholar 

  3. Jiang, H., Zhang, G., Wang, H., Bao, H.: Spatio-temporal video segmentation of static scenes and its applications. IEEE Trans. Multimedia 17, 3–15 (2015)

    Article  Google Scholar 

  4. Ngan, K.N., Li, H.: Video Segmentation and Its Applications. Springer Science & Business Media, Beriln (2011)

    Book  Google Scholar 

  5. Petersohn, C.: Temporal Video Segmentation. Jörg Vogt Verlag, Dresden (2010)

    Google Scholar 

  6. Cirne, M.V.M., Pedrini, H.: Summarization of videos by image quality assessment. In: Bayro-Corrochano, E., Hancock, E. (eds.) CIARP 2014. LNCS, vol. 8827, pp. 901–908. Springer, Heidelberg (2014). doi:10.1007/978-3-319-12568-8_109

    Google Scholar 

  7. Cotsaces, C., Nikolaidis, N., Pitas, I.: Video shot boundary detection and condensed representation: a review. IEEE Signal Process. Mag. 23, 28–37 (2006)

    Article  Google Scholar 

  8. Apostolidis, E., Mezaris, V.: Fast shot segmentation combining global and local visual descriptors. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6583–6587 (2014)

    Google Scholar 

  9. Birinci, M., Kiranyaz, S.: A perceptual scheme for fully automatic video shot boundary detection. Sig. Process. Image Commun. 29, 410–423 (2014)

    Article  Google Scholar 

  10. Jiang, X., Sun, T., Liu, J., Chao, J., Zhang, W.: An adaptive video shot segmentation scheme based on dual-detection model. Neurocomputing 116, 102–111 (2013)

    Article  Google Scholar 

  11. Lu, Z.M., Shi, Y.: Fast video shot boundary detection based on SVD and pattern matching. IEEE Trans. Image Process. 22, 5136–5145 (2013)

    Article  MathSciNet  Google Scholar 

  12. Tippaya, S., Sitjongsataporn, S., Tan, T., Chamnongthai, K., Khan, M.: Video shot boundary detection based on candidate segment selection and transition pattern analysis. In: IEEE International Conference on Digital Signal Processing, pp. 1025–1029 (2015)

    Google Scholar 

  13. Whitehead, A., Bose, P., Laganiere, R.: Feature based cut detection with automatic threshold selection. In: Enser, P., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 410–418. Springer, Heidelberg (2004). doi:10.1007/978-3-540-27814-6_49

    Chapter  Google Scholar 

  14. Guimarães, S., Patrocínio, Z., Paula, H., Silva, H.: A new dissimilarity measure for cut detection using bipartite graph matching. Int. J. Semant. Comput. 03, 155–181 (2009)

    Article  Google Scholar 

  15. Almeida, J., Leite, N.J., S. Torres, R.: Rapid cut detection on compressed video. In: San Martin, C., Kim, S.-W. (eds.) CIARP 2011. LNCS, vol. 7042, pp. 71–78. Springer, Heidelberg (2011). doi:10.1007/978-3-642-25085-9_8

    Chapter  Google Scholar 

  16. Pal, G., Acharjee, S., Rudrapaul, D., Ashour, A.S., Dey, N.: Video segmentation using minimum ratio similarity measurement. Int. J. Image Min. 1, 87–110 (2015)

    Article  Google Scholar 

  17. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)

    Article  Google Scholar 

  18. Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, vol, 2, pp. 1398–1402 (2003)

    Google Scholar 

  19. Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2, 37–63 (2011)

    Google Scholar 

Download references

Acknowledgments

The authors are thankful to FAPESP (grants #2011/22749-8 and 2015/12228-1) and CNPq (grant #305169/2015-7) for their financial support.

Author information

Authors and Affiliations

  1. Institute of Computing, University of Campinas, Campinas, São Paulo, 13083-852, Brazil

    Anderson Carlos Sousa e Santos & Helio Pedrini

Authors
  1. Anderson Carlos Sousa e Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Helio Pedrini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Helio Pedrini .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Sousa e Santos, A.C., Pedrini, H. (2016). Adaptive Video Transition Detection Based on Multiscale Structural Dissimilarity. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50832-0_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation