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A TV Commercial Detection System

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6988))

Abstract

Automatic real-time recognition of TV commercials is an essential step for TV broadcast monitoring. It comprises of two basic tasks: rapid detection of known commercials that are stored in a database, and accurate recognition of unknown ones that appear for the first time in TV streaming. In this paper, we present the framework of a TV commercial detection system.

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References

  1. Albiol, A., Fulla, M.J., Albiol, A., Torres, L.: Detection of TV Commercials. In: International Conference on Acoustics, Speech and Signal Processing, pp. 541–544 (2004)

    Google Scholar 

  2. Angihotri, L., Dimitrov, N., McGee, T., Jeannin, S., Schaffer, D., Nesvadba, J.: Evolvable Visual Commercial Detector. In: Proc. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), vol. 2, pp. 79–84 (2003)

    Google Scholar 

  3. Ardizzone, E., et al.: Content-based indexing if image and video database by global and shape features. In: Proc. Of the International Conference on Pattern Recognition (1996)

    Google Scholar 

  4. Chang, C.L.E., Wang, J., Wiederhold, G.: Rime: A replicated image detector for the World Wide Web. In: SPIE Multimedia Storage and Archiving Systems III (1998)

    Google Scholar 

  5. Chang, H.S., Sull, S., Lee, S.U.: Efficient video indexing scheme for content-based retrieval. IEEE Trans. Circuits Syst. Video Technology (1999)

    Google Scholar 

  6. Cheung, S.-C.S., Zakhor, A.: Efficient video similarity measurement and search. In: Proc. of International Conference on Image Processing, British Columbia, Canada, pp. 85–89 (2000)

    Google Scholar 

  7. Duan, L.Y., Wang, J., Zheng, Y., Jin, J.S., Lu, H., Xu, C.: Segmentation, categorization, and identification of commercial clips from TV streams using multimodal analysis. In: ACM Multimedia, pp. 201–210 (2006)

    Google Scholar 

  8. Gauch, J.M., Shivadas, A.: Finding and identifying unknown commercials using repeated video sequence detection. In: Computer Vision and Image Understanding, vol. 103, pp. 80–88. Elsevier Science Inc., New York (2006)

    Google Scholar 

  9. Hampapur, A., Bolle, R.M.: Feature based indexing for media tracking. In: Proc. of Int. Conf. on Multimedia and Expo, pp. 67–70 (2000)

    Google Scholar 

  10. Hampapur, A., Hyun, K., Bolle, R.M.: Comparison of sequence matching techniques for video copy detection. In: Proc. SPIE, vol. 4676, pp. 194–201 (2002)

    Google Scholar 

  11. http://www.11.com/

  12. Herley, C.: Accurate repeat finding and object skipping using fingerprints. In: ACM Multimedia, pp. 656–665 (2005)

    Google Scholar 

  13. Herley, C.: ARGOS: automatically extracting repeating objects from multimedia streams. IEEE Transactions on Multimedia 8, 115–129 (2006)

    Article  Google Scholar 

  14. Hua, X., Lu, L., Zhang, H.: Robust Learning-Based TV Commercial Detection. In: ICME, pp. 149–152 (2005)

    Google Scholar 

  15. Hua, X., Chen, X., Zhang, H.: Robust Video Signature Based on Ordinal Measure. In: International Conference on Image Processing (ICIP), pp. 415–423 (1998)

    Google Scholar 

  16. http://www.16.com.au/

  17. Kashino, K., Kurozumi, T., Murase, H.: A quick search method for audio and video signals based on histogram pruning. IEEE Transactions on Multimedia 5, 348–357 (2003)

    Article  Google Scholar 

  18. Li, Y., Jin, J.S., Zhou, X.: Video Matching Using Binary Signature. In: Proceedings of the 2005 International Symposium on Intelligent Signal Processing and Communications Systems (ISPAC 2005), Hong Kong, December 13-16, pp. 317–320 (2005)

    Google Scholar 

  19. Li, Y., Jin, J.S., Zhou, X.: Matching Commercial Clips from TV Streams Using a Unique,Robust and Compact Signature. In: DICTA, Australia, pp. 266–272 (2005)

    Google Scholar 

  20. Lienhart, R., Kuhmunch, C., Effelsberg, W.: On the detection and recognition of television commercials. In: ICMCS, pp. 509–516. IEEE Computer Society, Los Alamitos (1997)

    Google Scholar 

  21. O’Connor, B.C.: Selecting key frames of moving image document: A digital environment for analysis and navigation. Microcomputers for Information Management 8(2), 119–133 (1991)

    Google Scholar 

  22. Nafeh, J.: Method and Apparatus for Classifying patterns of Television Programs and Commercials Based on Discerning of Broadcast Audio and Video Signal, US patent 5, 343, 251 (1994)

    Google Scholar 

  23. Sanchez, J.M., Binefa, X., Radeva, P.: Local colour analysis for scene break detection applied to tv commercial recognition. In: Proc. of Visual 1999, pp. 237–244 (1999)

    Google Scholar 

  24. Sanchez, J.M., Binefa, X., Vitria, J.: Shot Partitioning Based Recognition of TV Commercials. In: Multimedia Tools Applications, vol. 18, pp. 233–247. Kluwer Academic Publishers, Hingham (2002)

    Google Scholar 

  25. Shen, H.T., Ooi, B.C., Zhou, X.: Towards Effective Indexing for Very Large Video Sequence Database. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 730–741 (2005)

    Google Scholar 

  26. Tonomura, Y., Abe, S.: Content orient visual interface using video icons for visual database systems. Journal of Visual Languages and Computing’ 1, 183–198 (1990)

    Article  Google Scholar 

  27. Vilanilam, J.V., Varghese, A.K.: Advertising basics! A resource guide for beginners. Response Books, New Delhi (2004)

    Google Scholar 

  28. Wolf, W.: Key frame selection by motion analysis. In: Proc. ICASSP 1996, vol. II, pp. 1228–1231 (1996)

    Google Scholar 

  29. Zhuang, Y., Rui, Y., Huang, T.S., Mehrotra, S.: Adaptive key frame extraction using unsupervised clustering. In: Proc. IEEE ICIP 1998, vol. 1, pp. 866–870 (1998)

    Google Scholar 

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Li, Y., Luo, S. (2011). A TV Commercial Detection System. In: Gong, Z., Luo, X., Chen, J., Lei, J., Wang, F.L. (eds) Web Information Systems and Mining. WISM 2011. Lecture Notes in Computer Science, vol 6988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23982-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-23982-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23981-6

  • Online ISBN: 978-3-642-23982-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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