Classification of Semantic Concepts to Support the Analysis of the Inter-cultural Visual Repertoires of TV News Reviews

  • Martin Stommel
  • Martina Duemcke
  • Otthein Herzog
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7006)


TV news reviews are of strong interest in media and communication sciences, since they indicate national and international social trends. To identify such trends, scientists from these disciplines usually work with manually annotated video data. In this paper, we investigate if the time-consuming process of manual annotation can be automated by using the current pattern recognition techniques. To this end, a comparative study on different combinations of local and global features sets with two examples of the pyramid match kernel is conducted. The performance of the classification of TV new scenes is measured. The classes are taken from a coding scheme that is the result of an international discourse in media and communication sciences. For the classification of studio vs. non-studio, football vs. ice hockey, computer graphics vs. natural scenes and crowd vs. no crowd, recognition rates between 80 and 90 percent could be achieved.


Semantic Concept Interest Point Detector Maximally Stable Extremal Region Pyramid Match Kernel Global Colour Histogram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Stommel, M., Duemcke, M., Herzog, O.: Classification of Semantic Concepts to Support the Analysis of the Inter-Cultural Visual Repertoires of TV News Reviews. Technical Report 58, Center for Computing and Communication Technologies, University Bremen, Germany (2011)Google Scholar
  2. 2.
    Ludes, P.: Visual Hegemonies: An Outline = Volume 1 of The World Language of Key Visuals: Computer Sciences, Humanities, Social Sciences. LIT, Muenster (2005) (Translations into Portuguese in 2007 and Chinese in 2008)Google Scholar
  3. 3.
    Hanitzsch, T.: Codebook for Content Analysis Foreign TV News Project. Worlds of Journalisms Project (February 2010)Google Scholar
  4. 4.
    Dorai, C., Venkatesh, S.: Bridging the Semantic Gap in Content Management Systems: Computational Media Aesthetics. In: Computational Semiotics (COSIGN), pp. 94–99 (2001)Google Scholar
  5. 5.
    Smeaton, A.F., Over, P., Kraaij, W.: High level feature detection from video in TRECVid: a 5-year retrospective of achievements. In: Divakaran, A. (ed.) Multimedia Content Analysis, Theory and Applications. Springer, Heidelberg (2008)Google Scholar
  6. 6.
    Hauptmann, A., Lin, W.H., Yan, R.: How Many High-level Concepts Will Fill the Semantic Gap in News Video Retrieval? In: Proceedings of ACM International Conference on Image and Video Retrieval, pp. 627–634 (2007)Google Scholar
  7. 7.
    Garg, R., Du, H., Seitz, S.M., Snavely, N.: The Dimensionality of Scene Appearance. In: IEEE International Conference on Computer Vision, ICCV (2009)Google Scholar
  8. 8.
    Ke, Y., Sukthankar, R.: PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. In: Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 506–513 (2004)Google Scholar
  9. 9.
    Jain, A.K., Duin, R., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)CrossRefGoogle Scholar
  10. 10.
    Stommel, M., Herzog, O.: Sift-based object recognition with fast alphabet creation and reduced curse of dimensionality. In: Int’l Conf. on Image and Vision Computing New Zealand, IVCNZ (2009)Google Scholar
  11. 11.
    Crandall, D.J., Felzenszwalb, P.F., Huttenlocher, D.P.: Spatial Priors for Part-Based Recognition Using Statistical Models. In: Computer Vision and Pattern Recognition (CVPR), pp. 10–17 (2005)Google Scholar
  12. 12.
    Stommel, M., Kuhnert, K.D.: Visual Alphabets on Different Levels of Abstraction for the Recognition of Deformable Objects. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds.) SSPR&SPR 2010. LNCS, vol. 6218, pp. 213–222. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Grauman, K., Darrell, T.: The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. In: IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 1458–1465 (2005)Google Scholar
  14. 14.
    Grauman, K., Darrell, T.: Approximate correspondences in high dimensions. In: Advances in Neural Information Processing Systems, NIPS (2006)Google Scholar
  15. 15.
    Bay, H., Ess, A., Tuytelaars, T., van Gool, L.: SURF: Speeded Up Robust Features. Computer Vision and Image Understanding (CVIU) 110(3), 346–359 (2006)CrossRefGoogle Scholar
  16. 16.
    Forssen, P.E.: Maximally stable colour regions for recognition and matching. In: Computer Vision and Pattern Recognition, CVPR (2007)Google Scholar
  17. 17.
    Mikolajczyk, K., Schmid, C.: An Affine Invariant Interest Point Detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Freeman, W.H., Adelson, E.H.: The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 891–906 (1991)CrossRefGoogle Scholar
  19. 19.
    Belongie, S., Mori, G., Malik, J.: Matching with shape contexts. In: IEEE Workshop on Content-based access of Image and Video-Libraries (CBAIVL), vol. 13, pp. 20–26 (2000)Google Scholar
  20. 20.
    Vezhnevets, V., Sazonov, V., Andreeva, A.: A Survey on Pixel-Based Skin Color Detection Techniques. In: Proc. Graphicon-2003, vol. 13, pp. 85–92 (2003)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Martin Stommel
    • 1
  • Martina Duemcke
    • 1
  • Otthein Herzog
    • 1
  1. 1.TZI Center for Computing and Communication TechnologiesUniversity BremenBremenGermany

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