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Image Browsing: Semantic Analysis of NNk Networks

  • Daniel Heesch
  • Stefan Rüger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3568)

Abstract

Given a collection of images and a set of image features, we can build what we have previously termed NN k networks by representing images as vertices of the network and by establishing arcs between any two images if and only if one is most similar to the other for some weighted combination of features. An earlier analysis of its structural properties revealed that the networks exhibit small-world properties, that is a small distance between any two vertices and a high degree of local structure. This paper extends our analysis. In order to provide a theoretical explanation of its remarkable properties, we investigate explicitly how images belonging to the same semantic class are distributed across the network. Images of the same class correspond to subgraphs of the network. We propose and motivate three topological properties which we expect these subgraphs to possess and which can be thought of as measures of their compactness. Measurements of these properties on two collections indicate that these subgraphs tend indeed to be highly compact.

Keywords

Average Distance Semantic Analysis Relevance Feedback Semantic Class Relevance Class 
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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References

  1. 1.
    Aho, A.V., Hopcroft, J.E., Ullman, J.D.: Data Structures and Algorithms. Addison Wesley, Reading (1983)zbMATHGoogle Scholar
  2. 2.
    Bach, J.R., Fuller, C., Gupta, A., Hampapur, A., Horowitz, B., Humphrey, R., Shu, C.: Virage image search engine: An open framework for image management. In: SPIE Conf on Storage and Retrieval for Image and Video Databases IV, vol. 2670, pp. 76–87 (1996)Google Scholar
  3. 3.
    Campbell, I.: The ostensive model of developing information needs. PhD thesis, University of Glasgow (2000)Google Scholar
  4. 4.
    Cox, K.: Information retrieval by browsing. In: Proc 5th Int’l Conf on New Information Technology (1992)Google Scholar
  5. 5.
    Croft, B., Parenty, T.: Comparison of a network structure and a database system used for document retrieval. Information Systems 10, 377–390 (1985)CrossRefGoogle Scholar
  6. 6.
    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. IEEE Computer, 23–32 (1995)Google Scholar
  7. 7.
    Forsyth, D., Malik, J., Fleck, M., Greenspan, H., Leung, T.: Finding pictures of objects in large collections of images. In: Int’l Workshop on Object Recognition for Computer Vision (1996)Google Scholar
  8. 8.
    Heesch, D., Pickering, M., Yavlinsky, A., Rüger, S.: Video retrieval within a browsing framework using keyframes. In: Proc TRECVID 2003 (2004)Google Scholar
  9. 9.
    Heesch, D., Rüger, S.: NNk networks for content-based image retrieval. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 253–266. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Heesch, D., Rüger, S.: Three interfaces for content-based access to image collections. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 491–499. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Niblack, W., Barber, R., Equitz, W., Flickner, M.D., Glasman, E.H., Petkovic, D., Yanker, P., Faloutsos, C., Taubin, G., Heights, Y.: Querying images by content, using color, texture, and shape. In: SPIE Conf on Storage and Retrieval for Image and Video Databases, vol. 1908, pp. 173–187 (1993)Google Scholar
  12. 12.
    Pentland, A., Picard, R.W., Sclaroff, S.: Photobook: content-based manipulation of image databases. Int’l Journal on Computer Vision 18(3), 233–254 (1996)CrossRefGoogle Scholar
  13. 13.
    Pickering, M., Rüger, S.: Evaluation of key-frame based retrieval techniques for video. Computer Vision and Image Understanding 92(1), 217–235 (2003)CrossRefGoogle Scholar
  14. 14.
    Rubner, Y., Guibas, L.J., Tomasi, C.: The earth mover’s distance, multi-dimensional scaling, and color-based image retrieval. In: DARPA Image Understanding Workshop (1997)Google Scholar
  15. 15.
    Rui, Y., Huang, T.S.: Optimizing learning in image retrieval. In: Proc IEEE Conf on Computer Vision and Pattern Recognition (2000)Google Scholar
  16. 16.
    Rui, Y., Huang, T.S., Mehrotra, S.: Content-based image retrieval with relevance feedback in mars. In: Proc IEEE Int’l Conf on Image Processing (1997)Google Scholar
  17. 17.
    Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans on Circuits and Video Technology (1998)Google Scholar
  18. 18.
    Santini, S., Gupta, A., Jain, R.: Emergent semantics through interaction in image databases. IEEE Trans on Knowledge and Data Engineering 13(3), 337–351 (2001)CrossRefGoogle Scholar
  19. 19.
    Santini, S., Jain, R.: Integrated browsing and querying for image databases. IEEE MultiMedia 7(3), 26–39 (2000)CrossRefGoogle Scholar
  20. 20.
    Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans on Pattern Analysis and Machine Intelligence 22(12), 1349–1379 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Daniel Heesch
    • 1
  • Stefan Rüger
    • 1
  1. 1.Department of ComputingImperial College LondonLondonUK

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