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A Fuzzy Logic Based Approach to Feedback Reinforcement in Image Retrieval

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Emerging Intelligent Computing Technology and Applications (ICIC 2009)

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Abstract

Nowadays, due to the spread of digital imaging technologies, the design of effective content based image retrieval (CBIR) systems is perceived by the research community as a primary problem. Various techniques such as clustering and relevance feedback were proposed to obtain a certain level of knowledge about a given image database. Often clustering techniques were used to obtain a first level characterization of the image database used to speed up the successive stage of queries. In this work the authors use the knowledge obtained using a fuzzy clustering algorithm to reinforce the user feedback. The system was tested on the Columbia Coil-20 image database and the obtained results seem to be encouraging.

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References

  1. Flickner, M., et al.: Query by Image and Video Content: The QBIC System. IEEE Computer 28(9) (1995)

    Google Scholar 

  2. Pentland, A., Picard, R.W., Sclaroff, S.: Photobook: Content-based Manipulation of Image Databases. In: Proc. SPIE Storage Retrieval Image Video Databases II, pp. 34–37 (1994)

    Google Scholar 

  3. Yumin, T., Lixia, M.: Image Retrieval Based on Multiple Features Using Wavelet. In: Proceedings of the 5th Int. Conf. on Computational Intelligence and Multimedia Applications, Xi’an China, pp. 137–143 (2003)

    Google Scholar 

  4. Bae, H.J., Jung, S.H.: Image Retrieval Using Texture Based on DCT. In: Proc. of Int. Conf. on Information, Comm. and Signal Processing, Singapore, pp. 1065–1068 (1997)

    Google Scholar 

  5. Gevers, T., Smeulders, A.W.M.: Image Search Engines: An Overview. In: Medioni, G., Kang, S.B. (eds.) Emerging Topics in Computer Vision. Prentice Hall, Englewood Cliffs (2004)

    Google Scholar 

  6. Amato, A., Calabrese, M., Di Lecce, V.: Relevance Feedback Oriented Cbir Interface For Semantic Discovery. Wseas Transactions on Computers 9(5), 1978–1985 (2006)

    Google Scholar 

  7. Santini, S., Jain, R.: The ‘El Niño’ Image Database System, In: IEEE International Conference on Multimedia Computing and Systems, Florence, Italy (1999)

    Google Scholar 

  8. Iqbal, Q., Aggarwal, K.: Feature Integration, Multi-image Queries and Relevance Feedback in Image Retrieval. Invited Paper. To Appear, 6th International Conference on Visual Information Systems (VISUAL). Miami, Florida. pp. 24–26 (2003)

    Google Scholar 

  9. Boujemaa, N., Fauqueur, J., Ferecatu, M., Fleuret, F., Gouet, V., Le Saux, B., Sahbi, H.: Ikona: Interactive Generic and Specific Image Retrieval. In: International 24 Workshop on Multimedia Content-Based Indexing and Retrieval (MMCBIR), Rocquencourt, France, pp. 25–28 (2001)

    Google Scholar 

  10. Lay, J.A., Guan, L.: Image Retrieval Based on Energy Histograms of the Low Frequency DCT Coefficients. In: IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Phoenix USA, vol. 6, pp. 3009–3012 (1999)

    Google Scholar 

  11. Loupias, E., Sebe, N., Bres, S., Jolion, J.M.: Wavelet-based Salient Points for Image Retrieval. In: Int. Conf. on Image Processing. Vancouver BC Canada, vol. 2, pp. 518–521, 10–13 (2000)

    Google Scholar 

  12. Sikora, T.: The MPEG-7 Visual Standard for Content Description: an Overview. IEEE Transactions on Circuits and Systems for Video Technology 11(6), 696–702 (2001)

    Article  MathSciNet  Google Scholar 

  13. Zhang, D.: Improving Image Retrieval Performance by Using Both Color and Texture Features. In: Third Int. Conf. on Image and Graphics, Hong Kong, pp. 172–175 (2004)

    Google Scholar 

  14. Dorairaj, R., Namuduri, K.R.: Compact Combination of MPEG-7 Color and Texture Descriptors for Image Retrieval. In: Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, Pacific Grove California, vol. 1, pp. 387–391, 7–10 (2004)

    Google Scholar 

  15. Gevers, T., Smeulders, A.W.M.: PicToSeek: Combining Color and Shape Invariant Features for Image Retrieval. IEEE Transactions on Image Processing 9(1), 102–119 (2000)

    Article  Google Scholar 

  16. Frigui, H.: MembershipMap: Data Transformation Based on Granulation and Fuzzy Membership Aggregation. IEEE Transactions on Fuzzy Systems 14(6), 885–896 (2006)

    Article  Google Scholar 

  17. Liew, A.W.C., Leung, S.H., Lau, W.H.: Segmentation of Color Lip Images by Spatial Fuzzy Clustering. IEEE Transactions on Fuzzy Systems 11(4), 542–549 (2003)

    Article  Google Scholar 

  18. Eschrich, S., Hall, L.O., Ke, J.W., Goldgof, D.B.: Fast Accurate Fuzzy Clustering through Data Reduction. IEEE Transactions on Fuzzy Systems 11(2), 262–270 (2003)

    Article  Google Scholar 

  19. Amato, A., Di Lecce, V.: A Knowledge Based Approach for a Fast Image Retrieval System. Image and Vision Computing 26(11), 1466–1480 (2008)

    Article  Google Scholar 

  20. Di Lecce, V., Guerriero, A.: A Comparative Evaluation of Retrieval Methods for Duplicate Search in Image Database. Journal of Visual Languages and Computing 12, 105–120 (2001)

    Article  Google Scholar 

  21. Shen, X., Boutell, M., Luo, J., Brown, C.: Multi Label Machine Learning and its Application to Semantic Scene Classification. In: Proceedings of the 2004 International Symposium on Electronic Imaging, pp. 18–22 (2004)

    Google Scholar 

  22. Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (coil-100), Tech. Rep., Department of Computer Science, Columbia University (1996), http://www.cs.columbia.edu/CAVE/

  23. Le Saux, B., Boujemaa, N.: Unsupervised Robust Clustering for Image Database Categorization. In: Proc. 16th Int. Conf. on Pattern Recognition, Quebec, Canada, pp. 259–262 (2002)

    Google Scholar 

  24. Wang, Z., Feng, D., Chi, Z.: Comparison of Image Partition Methods for Adaptive Image Categorization Based on Structural Image Representation. In: IEEE 8th Int. Conf. on Control, Automation, Robotics and Vision, Kunming, China, pp. 676–680 (2004)

    Google Scholar 

  25. Müller, H., Marchand-Maillet, S., Pun, T.: The truth about corel - evaluation in image retrieval. In: Lew, M., Sebe, N., Eakins, J.P. (eds.) CIVR 2002. LNCS, vol. 2383, pp. 36–45. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  26. Pedrycz, W., Amato, A., Di Lecce, V., Piuri, V.: Fuzzy Clustering with Partial Supervision in Organization and Classification of Digital Image. IEEE Trans. on Fuzzy System. 16(4), 1008–1026 (2008)

    Article  Google Scholar 

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Di Lecce, V., Amato, A. (2009). A Fuzzy Logic Based Approach to Feedback Reinforcement in Image Retrieval. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_99

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  • DOI: https://doi.org/10.1007/978-3-642-04070-2_99

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04069-6

  • Online ISBN: 978-3-642-04070-2

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