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Journal of Intelligent Information Systems

, Volume 31, Issue 1, pp 53–84 | Cite as

Context-sensitive queries for image retrieval in digital libraries

  • G. Boccignone
  • A. Chianese
  • V. Moscato
  • A. Picariello
Article

Abstract

In this paper we show how to achieve a more effective Query By Example processing, by using active mechanisms of biological vision, such as saccadic eye movements and fixations. In particular, we discuss the way to generate two fixation sequences from a query image I q and a test image I t of the data set, respectively, and how to compare the two sequences in order to compute a similarity measure between the two images. Meanwhile, we show how the approach can be used to discover and represent the hidden semantic associations among images, in terms of categories, which in turn drive the query process.

Keywords

Animate vision Image retrieval Image indexing 

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References

  1. Baeza-Yates, R., Cunto, W., Manber, U, & Wu, S. (1994). Proximity matching using fixed-queries trees. In Proceedings of the Fifth Combinatorial Pattern Matching(CPM94), Lecture Notes in Computer Science, vol. 807 (pp. 198–212).Google Scholar
  2. Ballard, D. (1991). Animate vision. Artificial Intelligence, 48, 57–86. (London, UK: Springer)CrossRefGoogle Scholar
  3. Burkhard, W., & Keller, R. (1973). Some approaches to best-match file searching. Communications of the ACM, 16(4), 230–236.zbMATHCrossRefGoogle Scholar
  4. Banerjee, A., Dhillon, I. S., Ghosh, J., & Sra, S. (2003). Clustering on hyperspheres using expectation maximization. Technical report TR-03-07, Department of Computer Sciences, University of Texas, (February).Google Scholar
  5. Boccignone, G. Chianese, A., Moscato, V., & Picariello, A. (2005). Foveated Shot Detection for Video Segmentation. IEEE Transactions on Cicuits and Systems for Video Technology, 15(3), 365–377 (Marzo).CrossRefGoogle Scholar
  6. Carson, C., Belongie, S., Greenspan, H., & Malik, J. (2002). Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8), 1026–1038.CrossRefGoogle Scholar
  7. Celeux, G., & Govaert, G. (1992). A classification EM algorithm for clustering and two stochastic versions. Computational Statistics & Data Analysis, 14, 315–332.zbMATHCrossRefMathSciNetGoogle Scholar
  8. Chavez, E., Navarro, G., Baeza-Yates, R., & Marroquin, J. M. (2001). Searching in metric space. ACM Computing Surveys, 33, 273–321.CrossRefGoogle Scholar
  9. Ciaccia, P., Patella, M., & Zezula, P. (1997). M-tree: An efficient access method for similarity search in metric spaces. In Proc. of 23rd International Conference on VLDB, pp. 426–435.Google Scholar
  10. Colombo, C., Del Bimbo, A., & Pala, P. (1999). Semantics in visual information retrieval. IEEE MultiMedia, 6(3), 38–53.CrossRefGoogle Scholar
  11. Corridoni, J. M., Del Bimbo, A., & Pala, P. (1999). Image retrieval by color semantics, Multimedia Systems, 7(3), 175–183.CrossRefGoogle Scholar
  12. Del Bimbo, A., Mugnaini, M., Pala, P., & Turco, F. (1998). Visual querying by color perceptive regions. Pattern Recognition, 31(9), 1241–1253.CrossRefGoogle Scholar
  13. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) Maximum likelihood from incomplete data. Journal of the Royal Statistical Society, 39, 1–38.zbMATHMathSciNetGoogle Scholar
  14. Duygulu, P., Barnard, K., de Freitas, N., & Forsyth, D. (2002). Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In Seventh European Conference on Computer Vision, pp. 97–112.Google Scholar
  15. Djeraba, C. (2003). Association and content-based retrieval. IEEE Transactions on Knowledge and Data Engineering, 15(1), 118–135.CrossRefGoogle Scholar
  16. Edelman, S. (2002). Constraining the neural representation of the visual world. Trends in Cognitive Science, 6(3), 125–131.CrossRefGoogle Scholar
  17. Fan, W., Davidson, I., Zadrozny, B., & Yu, P. S. (2005). An improved categorization of classifier’s sensitivity on sample selection bias. In Proocedings of International Conference on Data Mining (ICDM05), pp. 605–608.Google Scholar
  18. Fryer, R. G., & Jackson, M. O. (2003). Categorical cognition: A psychological model of categories and identification in decision making. NBER Working Paper no. W9579, March.Google Scholar
  19. Hare, J. S., & Lewis, P. H. (2004). Salient regions for query by image content. Image and Video Retrieval (CIVR 2004), Dublin, Ireland, pp. 317–325, Springer ed.Google Scholar
  20. Hare, J. S. & Lewis, P. H. (2005). On image retrieval using salient regions with vector-spaces and latent semantics. Image and Video Retrieval (CIVR 2005), Singapore, Springer Ed.Google Scholar
  21. Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 1254–1259.CrossRefGoogle Scholar
  22. MacKay, D. J. C. (2003). Information theory, inference, and learning algorithms. UK: Cambridge University Press.zbMATHGoogle Scholar
  23. Mallat, S. (1998). A wavelet tour of signal processing. San Diego, CA: Academic Press.zbMATHGoogle Scholar
  24. MPEG-7 (1999). Visual part of eXperimentation Model (XM) version 2.0. MPEG-7 Output Document ISO/MPEG.Google Scholar
  25. Neal, R. M., & Hinton, G. E. (1998). A view of the EM algorithm that justifies incremental, sparse, and other variants. M. J. Jordan (Ed.), Learning in graphical models (pp. 355–368). Cambridge, MA: MIT.Google Scholar
  26. Newsam, S., Sumengen, B., & Manjunath, B. S. (2001). Category-based image retrieval. In International Conference on Image Processing (ICIP), pp. 596–599.Google Scholar
  27. Noton, D., & Stark, L. (1990). Scanpaths in the saccdice eye movements during pattern perception. Visual Research, 11, pp. 929–942.Google Scholar
  28. Santini, S. (2000). Evaluation vademecum for visual information systems. In Proc. of SPIE, vol. 3972. San Jose, USA.Google Scholar
  29. Santini, S., Gupta, A., & Jain, R. (2001). Emergent Semantics through Interactions in image databases. IEEE Transactions on Knowledge and Data Engineering, 13, 337–351.CrossRefGoogle Scholar
  30. Sebe, N., Tian, Q., Loupias, E., Lew, M., & Huang, T. (2003). Evaluation of salient point techniques. Image and Vision Computing, 21, 1087–1095.CrossRefGoogle Scholar
  31. Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 1349–1379.CrossRefGoogle Scholar
  32. Uhlmann, J. (1991). Satisfying general proximity/similarity queries with metric trees. Information Processing Letters, 40, 175–179.zbMATHCrossRefGoogle Scholar
  33. Walker-Smith, G. J., Gale, A. G., & Findlay, J. M. (1997). Eye movement strategies involved in face perception. Perception, 6, 313–326.CrossRefGoogle Scholar
  34. Wang, J. Z., Li, J., & Wiederhold, G. (2001). SIMPLIcity: Semantics-sensitive integrated matching for pictures libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 1–16, (Sept.)Google Scholar
  35. Yamanishi, K., Takeuchi, J.-I., Williams, G., & Melne, P. (2004). On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Mining and Knowledge Discovery, 8, 275–300.CrossRefMathSciNetGoogle Scholar
  36. Yu, D., & Zhang, A. (2003). ClusterTree: Integration of cluster representation and nearest-neighbor search for large data sets with high dimensions. IEEE Transactions on Knowledge and Data Engineering, 15(5), 1316–1337.CrossRefMathSciNetGoogle Scholar
  37. Zhong, S. & Ghosh, J. (2003). A unified framework for model-based clustering. Journal of Machine Learning Research, 4, 1001–1037.CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • G. Boccignone
    • 1
  • A. Chianese
    • 2
  • V. Moscato
    • 2
  • A. Picariello
    • 2
  1. 1.Dipartimento di Ingegneria dell’Informazione e Ingegneria ElettricaFisciano (SA)Italy
  2. 2.Dipartimento di Informatica e SistemisticaNaplesItaly

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