Skip to main content
SpringerLink
Log in

An automatic television stream structuring system for television archives holders

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Holders of extensive television archives have to describe television streams which are composed of various telecasts. The first step to this description is to break the stream into small logical units like telecasts or advertisements in order to describe each of them separately. In this article we focus on the television stream structuring which consists in finding automatically the beginning and end of the various telecasts. Since program guides do not include the real content of television streams, we propose an approach based on the modeling of television schedules. We aim for predicting all the possible television schedules for a particular day, so that the automatic system then needs only a few detections to obtain the accurate boundaries of each part of the stream.

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

Similar content being viewed by others

References

  1. Aigrain, P., Joly, P., Longueville, V.: Medium knowledge-based macrosegmentation of video into sequences. In: Intelligent Multimedia Information Retrieval, pp. 159–173 (1997)

  2. Albiol, A., Fulla, M., Albiol, A., Torres, L.: Detection of tv commercials. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (2004)

  3. Bengio Y., Frasconi P.: Input-output hmm’s for sequence processing. IEEE Trans. Neural Netw. 7, 1231–1249 (1996)

    Article  Google Scholar 

  4. Bennett, P.N.: Using asymmetric distributions to improve text classifier probability estimates. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2003)

  5. Boufounos, P., El-Difrawy, S., Ehrlich, D.: Hidden markov models for dna sequencing. In: Workshop on Genomic Signal Processing and Statistics 2002 (2002)

  6. Bourlard H., Bengio S.: Hidden Markov Models and other finite state automata for sequence processing, deuxième édition edn. MIT Press, London (2002)

    Google Scholar 

  7. Browne, P., Smeaton, A., Murphy, N., O’Connor, N., Marlow, S., Berrut, C.: Evaluating and combining digital video shot boundary detection algorithms. In: Irish Machine Vision and Image Processing Conference (2000)

  8. Carrive, J., Pachet, F., Ronfard, R.: Clavis—a temporal reasoning system for classification of audiovisual sequences. In: Proceedings of Content-Based Multimedia Information Access (RIAO) Conference. Paris, France (2000)

  9. Dempster A., Laird N., Rubin D.: Maximum-likelihood from incomplete data via the em algorithm. J. R. Stat. Soc. 39, 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  10. Divakaran, A., Miyaraha, K., Peker, K., Radhakrishnan, R., Xion, Z.: Video mining using combinations of unsupervised and supervised learning techniques. In: Proceedings of SPIE Conference on Storage and Retrieval for Multimedia Databases, pp. 235–243 (2004)

  11. Duffner, S., Garcia, C.: A neural scheme for robust detection of transparent logos in tv programs. In: Proceedings of the 16th International Conference on Artificial Neural Networks, pp. 14–23 (2006)

  12. Gales, M., Young, S.: The theory of segmental hidden markov models. Technical report, Université de Cambridge, Cambridge (1993)

  13. Garcia C., Delakis M.: Convolutional face finder: a neural architecture for fast and robust face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1408–1423 (2004)

    Article  Google Scholar 

  14. Glasberg, R., Samour, A., Elazouzi, K., Sikora, T.: Cartoon-recognition using visual-descriptors and a multilayer-perceptron. In: Proceedings of Workshop on Image Analysis for Multimedia Interactive Services (2005)

  15. Haupmann, A., Witbrock, M.: Story segmentation and detection of commercials in broadcast news video. In: Proceedings of the Advances in Digital Libraries Conference, pp. 168–180 (1998)

  16. Huang, J., Liu, Z., Rosenberg, A.: Automated semantic structure reconstruction and representation generation for broadcast news. In: Proceedings of SPIE Storage and Retrieval for Image and Video Databases VII, pp. 50–62 (1999)

  17. Kijak, E., Oisel, L., Gros, P.: Audiovisual integration for tennis broadcast structuring. In: Proceedings of the IEEE Third International Workshop on Content-Based Multimedia Indexing 2003 (2003)

  18. Li Y., Kuo C.J.: Video Content Analysis Using Multimodal Information for Movie Content Extraction, Indexing and Representation. Kluwer, Dordrecht (2003)

    Google Scholar 

  19. Liang, L., Lu, H., Xue, X., Tan, Y.: Program segmentation for tv videos. In: IEEE International Symposium on Circuits and Systems, pp. 1549–1552 (2005)

  20. Lienhart, R., Kuhmünch, C., Effelsberg, W.: On the detection and recognition of television commercials. In: Proceedings of the 1997 International Conference on Multimedia Computing and Systems, pp. 509–516 (1997)

  21. Mizutani, M., Ebadollahi, M., Chang, S.: Commercial detection in heterogeneous video streams using fused multi-modal and temporal features. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 157–160 (2005)

  22. Mongillo G., Deneve S.: Online learning with hidden markov models. Neural Comput. 20(7), 1706–1716 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  23. Moore, D., Essa, I.: Recognizing multitasked activities using stochastic context-free grammar. In: Proceedings of the 18th National Conference on Artificial Intelligence, pp. 770–776 (2002)

  24. Murphy, K.: Dynamic bayesian networks: representation, inference and learning. PhD Thesis, Université de Californie, Berkeley (2002)

  25. Naturel, X., Gros, P.: Fast structuring of large television streams using program guides. In: Proceedings of the 4th International Workshop on Adaptive Multimedia Retrieval (2006)

  26. Norris J.: Markov chains. Cambridge series in statistical and probabilistic mathematics. Cambridge University Press, Cambridge (1999)

    Google Scholar 

  27. Petersohn, C.: Fraunhofer heinrich hertz institute at trecvid 2004: shot boundary detection system. In: Proceedings of TRECVID 2004 (2004)

  28. Poli, J., Carrive, J.: Tv stream structuring with program guides. In: Proceedings of the 8th IEEE Symposium on Multimedia, pp. 329–334 (2006)

  29. Poli, J., Philippeau, J., Pinquier, J., Carrive, J.: Fast hierarchical structuring of morning drives. In: Proceedings of IEEE Fifth International Workshop on Content-Based Multimedia Indexing 2007(à paraître) (2007)

  30. Pua K., Gauch J.M., Gauch S., Miadowicz J.Z.: Real time repeated video sequence identification. Comput. Vis. Image Underst. 93(3), 310–327 (2004)

    Article  Google Scholar 

  31. Puterman, M.: Markov Decision Processes. Wiley Series in Probability and Mathematical Statistics. Wiley (1994)

  32. Quinlan, J.: Learning with continuous classes. In: Proceedings of Artificial Intelligence Conference, pp. 343–348 (1992)

  33. Rabiner L.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)

    Article  Google Scholar 

  34. Rapantzikos, K., Avrithis, Y., Kollias, S.: On the use of spatiotemporal visual attention for video classification. In: Proceedings of International Workshop on Very Low Bitrate Video Coding (2005)

  35. Richard M., Lippmann R.: Neural network classifiers estimate bayesian a posteriori probabilities. Neural comput. 3, 461–483 (1991)

    Article  Google Scholar 

  36. Rui, Y., Huang, T., Mehrotra, S.: Exploring video structure beyond the shots. In: Proceedings of IEEE International Conference on Multimedia Computing and Systems, pp. 237–240 (1998)

  37. Sadlier, D., Marlow, S., O’Connor, N., Murphy, N.: Automatic tv advertisement detection from mpeg bitstream. In: Proceedings of the 1st International Workshop on Pattern Recognition in Information Systems, pp. 14–25 (2001)

  38. Sadlier, D., O’Connor, N.: Event detection in field sports video using audio-visual features and a support vector machine. In: IEEE Transactions on Circuits Systems and Video Technology, pp. 1225–1233 (2005)

  39. Shivadas, A., Gauch, J.: Real-time commercial recognition using color moments and hashing. In: Proceedings of ACM SIGMM International Workshop on Multimedia Information Retrieval (2006)

  40. Snoek C., Worring M.: Multimodal video indexing: a review of the state-of-the-art. Multimed. Tools Appl. 25(1), 5–35 (2005)

    Article  Google Scholar 

  41. Sundaram, H., Chang, S.: Audio scene segmentation using multiple features, models, and time scales. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing 2000 (2000)

  42. Sundaram H., Chang S.: Computable scenes and structures in films. IEEE Trans. Multimed. 4(4), 482–491 (2002)

    Article  Google Scholar 

  43. Vasconcelos N., Lippman A.: Statistical models of video structure for content analysis and characterization. IEEE Trans. Image Process. 9(1), 3–19 (2000)

    Article  Google Scholar 

  44. Veneau, E., Ronfard, R., Bouthemy, P.: From video shot clustering to sequence segmentation. In: Proceedings of International Conference on Pattern Recognition, pp. 254–257 (2000)

  45. Weber K., Ikbal S., Bengio S., Bourlard H.: Robust speech recognition and feature extraction using hmm2. New Comput. Paradigms Acoust. Model. Speech Recognit. 17, 195–211 (2003)

    Google Scholar 

  46. Yeung, M., Yeo, B., Liu, B.: Extracting story units from long programs for video browsing and navigation. In: Proceedings of the 1996 International Conference on Multimedia Computing and Systems, pp. 296–305 (1996)

Download references

Author information

Authors and Affiliations

  1. Institut National de l’Audiovisuel, 4 Avenue de l’Europe, 94366, Bry-sur-Marne Cedex, France

    Jean-Philippe Poli

Authors
  1. Jean-Philippe Poli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jean-Philippe Poli.

Additional information

Communicated by A. Mauthe.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Poli, JP. An automatic television stream structuring system for television archives holders. Multimedia Systems 14, 255–275 (2008). https://doi.org/10.1007/s00530-008-0140-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-008-0140-2

Keywords

Search

Navigation