Classified Ranking of Semantic Content Filtered Output Using Self-organizing Neural Networks

  • Marios Angelides
  • Anastasis Sofokleous
  • Minaz Parmar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


Cosmos-7 is an application that can create and filter MPEG-7 semantic content models with regards to objects and events, both spatially and temporally. The results are presented as numerous video segments that are all relevant to the user’s consumption criteria. These results are not ranked to the user’s ranking of relevancy, which means the user must now laboriously sift through them. Using self organizing networks we rank the segments to the user’s preferences by applying the knowledge gained from similar users’ experience and use content similarity for new segments to derive a relative ranking.


Semantic Content Collaborative Filter Video Segment Similar User Filter Criterion 


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  1. 1.
    Angelides, M.C., Agius, H.W.: An MPEG-7 Scheme for Semantic Content Modeling and Filtering of Digital Video. In: ACM Multimedia Systems (2006)Google Scholar
  2. 2.
    ISO/IEC: Information Technology −Multimedia Content Description Interface – Part 5: Multimedia Description Schemes. Geneva, Switzerland, International Organisation for Standardisation (2002)Google Scholar
  3. 3.
    Koprinska, I., Carrato, S.: Temporal video segmentation: A survey. Signal Processing: Image Communication 16(5), 477–500 (2001)CrossRefGoogle Scholar
  4. 4.
    Assfalg, J., Bertini, M., Colombo, C., Bimbo, A.D.: Semantic annotation of sports videos. Multimedia, IEEE 9, 52–60 (2002), Available, Scholar
  5. 5.
    Troncy, R.: Integrating Structure and Semantics into Audio-visual Documents. In: Proc. of 2nd International Semantic Web Conference (ISWC), Sanibel Island, Florida, USA, October 20-23, pp. 566–581 (2003)Google Scholar
  6. 6.
    Tran-Thuong, T., Roisin, C.: A multimedia model based on structured media and subelements for complex multimedia authoring and presentation. In: IJSEKE, vol. 12, pp. 473–500 (2002)Google Scholar
  7. 7.
    Wenyin, L., Chen, Z., Lin, F., Zhang, H., Ma, W.-Y.: Ubiquitous media agents: a framework or managing personally accumulated multimedia files. Multimedia Syst. 9(2), 144–156 (2003)CrossRefGoogle Scholar
  8. 8.
    Ferman, A.M., Beek, J.H.E.P.V., Sezan, M.I.: Content-based filtering and personalization using structured metadata. In: Proceedings of the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries, Portland, Oregon, p. 393 (2002)Google Scholar
  9. 9.
    Wallace, M.S.G.: Towards a context aware mining of user interests for consumption of multimedia documents. In: Proceedings of the 2002 IEEE International Conference on Multimedia and Expo, vol. 1, pp. 733–736 (2002)Google Scholar
  10. 10.
    Eirinaki, M., Vazirgiannis, M.: Web mining for web personalization. ACM Trans. Internet Technol. 3(1), 1–27 (2003)CrossRefGoogle Scholar
  11. 11.
    Lee, M., Choi, P., Woo, Y.E.: A Hybrid Recommender System Combining Collaborative Filtering with Neural Network. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 531–534. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Novak, J., Wurst, M., Fleischmann, M., Strauss, W.: Discovering, Visualizing and Sharing Knowledge through Personalized Learning Knowledge Maps, Spring Symposium on Agent-Mediated Knowledge Management, Technical Report SS-03-01, Stanford University, pp. 101-108 (2003)Google Scholar
  13. 13.
    Tan, A.-H., Pan, H.: Adding personality to information clustering. In: Proceedings of the Sixth Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 251–256 (2002)Google Scholar
  14. 14.
    Ultsch, A.: Self Organizing Neural Networks perform different from statistical k-means clustering. In: Proccedings of GfKl, Basel, Swiss (1995)Google Scholar
  15. 15.
    Rangarajan, S.K., Phoha, V.V., Balagani, K.S., Selmic, R.R., Iyengar, S.S.: Adaptive Neural Network Clustering of Web Users. Computer 37(4), 34–40 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marios Angelides
    • 1
  • Anastasis Sofokleous
    • 1
  • Minaz Parmar
    • 1
  1. 1.Brunel UniversityUxbridge, LondonUK

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