Abstract
This paper describes a novel interaction technique to support content-based image search in large image collections. The idea is to represent each image as a vertex in a directed graph. Given a set of image features, an arc is established between two images if there exists at least one combination of features for which one image is retrieved as the nearest neighbour of the other. Each arc is weighted by the proportion of feature combinations for which the nearest neighour relationship holds. By thus integrating the retrieval results over all possible feature combinations, the resulting network helps expose the semantic richness of images and thus provides an elegant solution to the problem of feature weighting in content-based image retrieval. We give details of the method used for network generation and describe the ways a user can interact with the structure. We also provide an analysis of the network’s topology and provide quantitative evidence for the usefulness of the technique.
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References
Adamic, L.A., Lukose, R.M., Puniyani, A.R., Huberman, B.A.: Search in power-law networks. Physical Review E 64 (2001)
Bollobás, B.: Random Graphs. Springer, New York (1985)
Campbell, I.: The ostensive model of developing information-needs. PhD thesis, University of Glasgow (2000)
Chen, C., Gagaudakis, G., Rosin, P.: Similarity-based image browsing. In: Proceedings of the 16th IFIP World Computer Congress. International Conference on Intelligent Information Processing (2000)
Cox, K.: Information retrieval by browsing. In: Proceedings of The 5th International Conference on New Information Technology, Hong kong (1992)
Cox, K.: Searching through browsing. PhD thesis, University of Canberra (1995)
Croft, B., Parenty, T.J.: Comparison of a network structure and a database system used for document retrieval. Information Systems 10, 377–390 (1985)
Dearholt, D.W., Schvaneveldt, R.W.: Properties of Pathfinder networks. In: Schvaneveldt, R.W. (ed.) Pathfinder associative networks: Studies in knowledge organization, Ablex, Norwood (1990)
Fowler, R.H., Wilson, B., Fowler, W.A.L.: Information navigator: An information system using associative networks for display and retrieval. Department of Computer Science, Technical Report NAG9-551, 92-1 (1992)
Heesch, D., Pickering, M., Yavlinsky, A., Rüger, S.: Video retrieval within a browsing framework using keyframe. In: Proceedings of TRECVID 2003, NIST, Gaithersburg, MD, November 2003 (2004)
Heesch, D.C., Rüger, S.: Performance boosting with three mouse clicks — relevance feedback for CBIR. In: Proceedings of the European Conference on IR Research 2003. LNCS, Springer, Heidelberg (2003)
Heesch, D.C., Yavlinsky, A., Rüger, S.: Performance comparison between different similarity models for CBIR with relevance feedback. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, Springer, Heidelberg (2003)
Kleinberg, J.M.: Navigation in a small world. Nature, 845 (2000)
Manjunath, B.S., Ohm, J.-S.: Color and texture descriptors. IEEE Transactions on circuits and systems for video technology 11, 703–715 (2001)
Müller, H., Müller, W., Squire, D.M., Marchand-Maillet, M.S., Pun, T.: Strategies for positive and negative relevance feedback in image retrieval. In: Proceedings of the 15th International Conference on Pattern Recognition (ICPR 2000), IEEE, Barcelona (2000)
Newman, M.E.J.: Random graphs as models of networks. In: Bornholdt, S., Schuster, H.G. (eds.) Handbook of graphs and networks - from the genome to the internet, Wiley-VCH, Chichester (2003)
Santini, S., Gupta, A., Jain, R.: Emergent semantics through interaction in image databases. IEEE transactions on knowledge and data engineering 13(3), 337–351 (2001)
Santini, S., Jain, R.: Integrated browsing and querying for image databases. IEEE MultiMedia 7(3), 26–39 (2000)
Tieu, K., Viola, P.: Boosting image retrieval. In: 5th International Conference on Spoken Language Processing (December 2000)
van Dongen, S.: A cluster algorithm for graphs. Technical report, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam (2000)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393, 440–442 (1998)
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Heesch, D., Rüger, S. (2004). NNk Networks for Content-Based Image Retrieval. In: McDonald, S., Tait, J. (eds) Advances in Information Retrieval. ECIR 2004. Lecture Notes in Computer Science, vol 2997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24752-4_19
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DOI: https://doi.org/10.1007/978-3-540-24752-4_19
Publisher Name: Springer, Berlin, Heidelberg
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