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Machine Learning Techniques for Multimedia pp 115–138Cite as

Online Content-Based Image Retrieval Using Active Learning

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Part of the book series: Cognitive Technologies ((COGTECH))

Abstract

Content-based image retrieval (CBIR) has attracted a lot of interest in recent years. When considering visual information retrieval in image databases, many difficulties arise. Learning is definitively considered as a very interesting issue to boost the efficiency of information retrieval systems. Different strategies, such as offline supervised learning or semi-supervised learning, have been proposed. Active learning methods have been considered with an increased interest in the statistical learning community. Initially developed in a classification framework, a lot of extensions are now proposed to handle multimedia applications. The purpose of this chapter is to present an overview of the online image retrieval systems based on supervised classification techniques. This chapter also provides algorithms in a statistical framework to extend active learning strategies for online content-based image retrieval.

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References

  1. S. Aksoy and R.M. Haralick. Feature normalization and likelihood-based similarity measures for image retrieval. Pattern Recognition Letters, 22:563–582, 2001.

    Article  MATH  Google Scholar 

  2. K Brinker. Incorporating diversity in active learning with support vector machines. In International Conference on Machine Learning, pages 59–66, February 2003.

    Google Scholar 

  3. E. Chang, B.T. Li, G. Wu, and K.S. Goh. Statistical learning for effective visual information retrieval. In IEEE International Conference on Image Processing, pages 609–612, Barcelona, Spain, September 2003.

    Google Scholar 

  4. O. Chapelle, P. Haffner, and V. Vapnik. Svms for histogram based image classification. IEEE Transactions on Neural Networks, 10:1055–1064, 1999.

    Article  Google Scholar 

  5. Y. Chen and J.Z. Wang. Image categorization by learning and reasoning with regions. International Journal on Machine Learning Research, 5:913–939, 2004.

    Google Scholar 

  6. L. Cinque, S. Levialdi, and A. Pellican? Color-based image retrieval using spatial-chromatic histograms. In IEEE International Conference on Multimedia Computing and Systems, 1999.

    Google Scholar 

  7. D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, pages 15(2):201–221, May 1994.

    Google Scholar 

  8. D. Cohn, Z. Ghahramani, and M. Jordan. Active learning with statistical models. Journal of Artificial Intelligence Research, 4:129–145, 1996.

    MATH  Google Scholar 

  9. M. Cord, J. Fournier, P.H. Gosselin, and S. Philipp-Foliguet. Interactive exploration for image retrieval. EURASIP Journal on Applied Signal Processing, 14:2173–2186, 2006.

    Google Scholar 

  10. M. Cord, P.H. Gosselin, and S. Philipp-Foliguet. Stochastic exploration and active learning for image retrieval. Image and Vision Computing, 25:14–23, 2006.

    Article  Google Scholar 

  11. M. Cuturi, J.P. Vert, O. Birkenes, and T. Matsui. A kernel for time series based on global alignments. In IEEE ICASSP, volume 2, pages 413–416, 2007.

    Google Scholar 

  12. A. Dong and B. Bhanu. Active concept learning in image databases. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 35:450–466, 2005.

    Article  Google Scholar 

  13. M. Everingham, A. Zisserman, C. Williams, L. Van Gool, M. Allan, C. Bishop, O. Chapelle, N. Dalal, T. Deselaers, G. Dorko, S. Duffner, J. Eichhorn, J. Farquhar, M. Fritz, C. Garcia, T. Griffiths, F. Jurie, D. Keysers, M. Koskela, J. Laaksonen, D. Larlus, B. Leibe, H. Meng, H. Ney, B. Schiele, C. Schmid, E. Seemann, J. Shawe-Taylor, A. Storkey, S. Szedmak, B. Triggs, I. Ulusoy, V. Viitaniemi, and J. Zhang. The 2005 pascal visual object classes challenge. In Proceedings of the First PASCAL Challenges Workshop, 2006.

    Google Scholar 

  14. J. Fournier, M. Cord, and S. Philipp-Foliguet. Back-propagation algorithm for relevance feedback in image retrieval. In International Conference in Image Processing (ICIP’01), volume 1, pages 686–689, Thessaloniki, Greece, October 2001.

    Google Scholar 

  15. J. Fournier, M. Cord, and S. Philipp-Foliguet. Retin: A content-based image indexing and retrieval system. Pattern Analysis and Applications Journal, Special issue on image indexation, 4(2/3):153–173, 2001.

    MATH  MathSciNet  Google Scholar 

  16. P.H. Gosselin and M. Cord. RETIN AL: An active learning strategy for image category retrieval. In IEEE International Conference on Image Processing, volume 4, pages 2219–2222, Singapore, October 2004.

    Google Scholar 

  17. K. Grauman and T. Darell. Efficient image matching with distribution of local invariant features. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, June 2005.

    Google Scholar 

  18. T. Joachims. Transductive inference for text classification using support vector machines. In Proceedings of 16th International Conference on Machine Learning, pages 200–209. Morgan Kaufmann, San Francisco, CA, 1999.

    Google Scholar 

  19. F. Jurie and B. Triggs. Creating efficient codebooks for visual recognition. In International Conference on Computer Vision and Pattern Recognition, 2005.

    Google Scholar 

  20. A. Khotanzad and Y.H. Hong. Invariant image recognition by zernike moments. IEEE Trans on. Pattern Analysis and Machine Intelligence, 12(5):489–497, 1990.

    Article  Google Scholar 

  21. R. Kondor and T. Jebara. A kernel between sets of vectors. In International Conference on Machine Learning (ICML), 2003.

    Google Scholar 

  22. A. Krogh and J. Vedelsby. Neural network ensembles, cross validation, and active learning. In Advances in Neural Information Processing Systems, pages 7:231–238, 1995.

    Google Scholar 

  23. D. D. Lewis and J. Catlett. Heterogeneous uncertainly sampling for supervised learning. In W. W. Cohen and H. Hirsh, editors, International Conference on Machine Learning, pages 148–56. Morgan Kaufmann Publishers, San Francisco July 1994.

    Google Scholar 

  24. Y. Linde, A. Buzo, and R.M. Gray. An algorithm for vector quantizer design. IEEE Transaction on Communication, 28:84–94, 1980.

    Article  Google Scholar 

  25. M. Lindenbaum, S. Markovitch, and D. Rusakov. Selective sampling for nearest neighbor classifiers. Machine Learning, pages 54(2):125–152, February 2004.

    Article  MATH  Google Scholar 

  26. F. Liu and R. W. Picard. Periodicity, directionality, and randomness: Wold features for image modeling nad retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7):722–733, July 1996.

    Article  Google Scholar 

  27. D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal on Computer Vision (IJCV), 2(60):91–110, 2004.

    Article  Google Scholar 

  28. B.S. Manjunath and W.Y. Ma. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence (Special Issue on Digital Libraries), 18(8):837–842, August 1996.

    Article  Google Scholar 

  29. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K.-R. Müller. Fisher discriminant analysis with kernels. In Y.-H. Hu, J. Larsen, E. Wilson, and S. Douglas, editors, Neural Networks for Signal Processing IX, pages 41–48. IEEE, 1999.

    Google Scholar 

  30. A. Mojsilovic and B. Rogowitz. Capturing image semantics with low-level descriptors. In International Conference in Image Processing (ICIP’01), volume 1, pages 18–21, Thessaloniki, Greece, October 2001.

    Google Scholar 

  31. N. Najjar, J.P. Cocquerez, and C. Ambroise. Feature selection for semi supervised learning applied to image retrieval. In IEEE ICIP, volume 2, pages 559–562, Barcelena, Spain, Sept. 2003.

    Google Scholar 

  32. G. Pass, R. Zabih, and J. Miller. Comparing images using color coherence vectors. In ACM International Multimedia Conference, 1996.

    Google Scholar 

  33. G. Patanè and M. Russo. The enhanced LBG algorithm. Neural Networks, 14(9):1219–1237, November 2001.

    Article  Google Scholar 

  34. J. Peng, B. Bhanu, and S. Qing. Probabilistic feature relevance learning for content-based image retrieval. Computer Vision and Image Understanding, 75(1-2):150–164, July-August 1999.

    Article  Google Scholar 

  35. N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. In International Conference on Machine Learning, 2001.

    Google Scholar 

  36. Y. Rubner. Perceptual Metrics for Image Database Navigation. PhD thesis, Stanford University, May 1999.

    Google Scholar 

  37. Y. Rui, T. Huang, S. Mehrotra, and M. Ortega. A relevance feedback architecture for content-based multimedia information retrieval systems. In IEEE Workshop on Content-Based Access of Image and Video Libraries, pages 92–89, 1997.

    Google Scholar 

  38. S. Santini, A. Gupta, and R. Jain. Emergent semantics through interaction in image databases. IEEE Transactions on Knowledge and Data Engineering, 13(3):337–351, 2001.

    Article  Google Scholar 

  39. B. Schölkopf and A. Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.

    Google Scholar 

  40. J. Shawe-Taylor and N. Cristianini. Kernel methods for Pattern Analysis. Cambridge University Press, ISBN 0-521-81397-2, 2004.

    Google Scholar 

  41. J.R. Smith and S.F. Chang. Visual SEEK: a fully automated content-based image query system. In ACM Multimedia Conference, pages 87–98, Boston, USA, November 1996.

    Google Scholar 

  42. M.A. Stricker and M. Orengo. Similarity of color images. In SPIE, Storage and Retrieval for Image Video Databases III, volume 2420, pages 381–392, 1995.

    Google Scholar 

  43. M.J. Swain and D.H. Ballard. Color indexing. International Journal of Computer Vision, 7(1):11–32, 1991.

    Article  Google Scholar 

  44. S. Tong and E. Chang. Support vector machine active learning for image retrieval. In ACM Multimedia, pages 107–118, 2001.

    Google Scholar 

  45. S Tong and D Koller. Support vector machine active learning with application to text classification. Journal of Machine Learning Research, 2:45–66, November 2001.

    Article  Google Scholar 

  46. V.N. Vapnik. Statistical learning theory. Wiley, New York, 1998.

    MATH  Google Scholar 

  47. N. Vasconcelos. Bayesian models for visual information retrieval. PhD thesis, Massachusetts Institute of Technology, 2000.

    Google Scholar 

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Author information

Authors and Affiliations

  1. LIP6, UPMC Paris 6, Paris, France

    Matthieu Cord

  2. ETIS, UCP Cergy, France

    Philippe-Henri Gosselin

Authors
  1. Matthieu Cord
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  2. Philippe-Henri Gosselin
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Editor information

Editors and Affiliations

  1. UPMC University, CNRS (UMR 7606) Lab. LIP6, 104 Avenue du Président Kennedy, 75016 Paris, France

    Matthieu Cord

  2. University College Dublin, School of Computer Science & Informatics, Belfield, Dublin 2, Ireland

    Pádraig Cunningham

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© 2008 Springer-Verlag Berlin Heidelberg

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Cord, M., Gosselin, PH. (2008). Online Content-Based Image Retrieval Using Active Learning. In: Cord, M., Cunningham, P. (eds) Machine Learning Techniques for Multimedia. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75171-7_5

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  • DOI: https://doi.org/10.1007/978-3-540-75171-7_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75170-0

  • Online ISBN: 978-3-540-75171-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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