Navidgator - Similarity Based Browsing for Image and Video Databases

  • Damian Borth
  • Christian Schulze
  • Adrian Ulges
  • Thomas M. Breuel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5243)

Abstract

A main problem with the handling of multimedia databases is the navigation through and the search within the content of a database. The problem arises from the difference between the possible textual description (annotation) of the database content and its visual appearance. Overcoming the so called - semantic gap - has been in the focus of research for some time. This paper presents a new system for similarity-based browsing of multimedia databases. The system aims at decreasing the semantic gap by using a tree structure, built up on balanced hierarchical clustering. Using this approach, operators are provided with an intuitive and easy-to-use browsing tool. An important objective of this paper is not only on the description of the database organization and retrieval structure, but also how the illustrated techniques might be integrated into a single system.

Our main contribution is the direct use of a balanced tree structure for navigating through the database of keyframes, paired with an easy-to-use interface, offering a coarse to fine similarity-based view of the grouped database content.

Keywords

hierarchical clustering image databases video databases browsing multimedia retrieval 

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References

  1. 1.
    Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by image and video content: the qbic system. Computer 28(9), 23–32 (1995)CrossRefGoogle Scholar
  2. 2.
    Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Contentbased image retrieval at the end ofthe early years. IEEE transactions on pattern analysis and machine intelligence 22(12), 1349–1379 (2000)CrossRefGoogle Scholar
  3. 3.
    Broecker, L., Bogen, M., Cremers, A.B.: Bridging the semantic gap in content-based image retrieval systems. In: Internet Multimedia Management Systems II. Volume 4519 of the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference, 54–62 (2001)Google Scholar
  4. 4.
    Zhao, R., Grosky, W.: Narrowing the semantic gap-improved text-based web document retrieval using visual features. Multimedia, IEEE Transactions on 4(2), 189–200 (2002)CrossRefGoogle Scholar
  5. 5.
    Zhao, R., Grosky, W.: Bridging the Semantic Gap in Image Retrieval. Distributed Multimedia Databases: Techniques and Applications (2003)Google Scholar
  6. 6.
    Dorai, C., Venkatesh, S.: Bridging the semantic gap with computational media aesthetics. Multimedia, IEEE 10(2), 15–17 (2003)CrossRefGoogle Scholar
  7. 7.
    de Rooij, O., Snoek, C.G.M., Worring, M.: Mediamill: semantic video search using the rotorbrowser. In: [7], p. 649 (2007)Google Scholar
  8. 8.
    Barecke, T., Kijak, E., Nurnberger, A., Detyniecki, M.: Videosom: A som-based interface for video browsing. Image And Video Retrieval, Proceedings 4071, 506–509 (2006)CrossRefGoogle Scholar
  9. 9.
    Rautiainen, M., Ojala, T., Seppanen, T.: Cluster-temporal browsing of large news video databases. IEEE Int. Conference on Multimedia and Expo. 2, 751–754 (2004)Google Scholar
  10. 10.
    Chen, J., Bouman, C., Dalton, J.: Similarity pyramids for browsing and organization of large image databases. SPIE Human Vision and Electronic Imaging III 3299 (1998)Google Scholar
  11. 11.
    Chen, J.Y., Bouman, C., Dalton, J.: Hierarchical browsing and search of large image databases. Image Processing, IEEE Transactions on 9(3), 442–455 (2000)CrossRefGoogle Scholar
  12. 12.
    Taskiran, C., Chen, J., Albiol, A., Torres, L., Bouman, C., Delp, E.: Vibe: A compressed video database structured for active browsing and search. IEEE Transactions on Multimedia 6(1), 103–118 (2004)CrossRefGoogle Scholar
  13. 13.
    Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 32(3), 241–241 (1967)CrossRefGoogle Scholar
  14. 14.
    Manjunath, B., Ohm, J., Vasudevan, V., Yamada, A.: Color and texture descriptors. IEEE Trans. on Circuits Syst. for Video Techn. 11(6) (2001)Google Scholar
  15. 15.
    McQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  16. 16.
    Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6(2), 461–464 (1978)MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Borth, D., Ulges, A., Schulze, C., Breuel, T.M.: Keyframe extraction for video taggging and summarization. In: Informatiktage 2008, pp. 45–48 (2008)Google Scholar
  18. 18.
    Ulges, A., Schulze, C., Keysers, D., Breuel, T.M.: Content-based video tagging for online video portals. In: MUSCLE/Image-CLEF Workshop (2007)Google Scholar
  19. 19.
    Tamura, H., Mori, S., Yamawaki, T.: Textual features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics SMC-8(6) (1978)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Damian Borth
    • 1
  • Christian Schulze
    • 1
  • Adrian Ulges
    • 1
  • Thomas M. Breuel
    • 1
  1. 1.German Research Center for Artificial Intelligence (DFKI)University of KaiserslauternKaiserslauternGermany

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