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Knowledge Propagation in Collaborative Tagging for Image Retrieval

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Abstract

An important issue in current collaborative framework for media tagging is that some images or videos may not be annotated properly or even not annotated at all. In view of this, this paper proposes a new knowledge propagation scheme to automatically propagate keywords from a subset of annotated images to the unannotated ones. The main idea is based on image content analysis and training of keyword classifiers. An evolutionary scheme is utilized to find the salient regions in the annotated images, and the importance of the other regions is estimated using one-class support vector machine (OCSVM). An ensemble of variable-length radial basis function (VLRBF)-based classifiers is trained based on the visual features of the annotated images. The trained classifiers are then used for knowledge propagation. Experimental results using 100 concept categories demonstrate the effectiveness of the proposed method.

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References

  1. Amarnath, G., & Ramesh, J. (1997). Visual information retrieval. Communications of the ACM, 40(5), 70–79. doi:10.1145/253769.253798.

    Article  Google Scholar 

  2. Cox, I. J., Miller, M. L., Minka, T. P., Papathomas, T. V., & Yianilos, P. N. (2000). The Bayesian image retrieval system, PicHunter: Theory, implementation, and psychophysical experiments. IEEE Transactions on Image Processing, 9(1), 20–37. doi:10.1109/83.817596.

    Article  Google Scholar 

  3. Flickher, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., et al. (1995). Query by image and video content: The QBIC system. IEEE Comput., 28(9), 23–32.

    Google Scholar 

  4. Gevers, T., & Smeulders, A. W. M. (2000). PicToSeek: Combining color and shape invariant features for image retrieval. IEEE Transactions on Image Processing, 9, 102–119. doi:10.1109/83.817602.

    Article  Google Scholar 

  5. Pentland, A., Picard, R. W., & Sclaroff, S. (1996). Photobook: Content-based manipulation of image databases. International Journal of Computer Vision, 18(3), 233–254. doi:10.1007/BF00123143.

    Article  Google Scholar 

  6. Rui, Y., Huang, T. S., Mehrotra, S. (1997). Content-based image retrieval with relevance feedback in MARS. Proc. IEEE Int. Conf. Image Processing, Washington D.C., USA, pp. 815–818.

  7. Smith, J. R., Chang, S. F. (1996). VisualSEEk: A fully automated content-based image query system. Proc. ACM Multimedia, pp. 87–98.

  8. Wu, K., & Yap, K. H. (2006). Fuzzy SVM for content-based image retrieval—A pseudo-label support vector machine framework. IEEE Comput. Intell. Mag., 1, 10–16.

    Google Scholar 

  9. Yap, K. H., & Wu, K. (2005). A soft relevance framework in content-based image retrieval systems. IEEE Transactions on Circuits and Systems for Video Technology, 15(12), 1557–1568. doi:10.1109/TCSVT.2005.856912.

    Article  Google Scholar 

  10. Muneesawang, P., & Guan, L. (2002). Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture. IEEE transactions on neural networks, 13(4), 821–834. doi:10.1109/TNN.2002.1021883.

    Article  Google Scholar 

  11. Muneesawang, P., & Guan, L. (2004). An interactive approach for CBIR using a network of radial basis functions. IEEE Transactions on Multimedia, 6(5), 703–716. doi:10.1109/TMM.2004.834866.

    Article  Google Scholar 

  12. Muneesawang, P., & Guan, L. (2005). Using knowledge of the region of interest (ROI) in automatic image retrieval learning. Proc. Int. Joint Conference on Neural Networks, 3, 1854–1859.

    Article  Google Scholar 

  13. Yu, Z. W., Wong, H. S. (2006). Approximate query processing for efficient content-based image retrieval based on a hierarchical SOM. Proc. Int. Joint Conference on Neural Networks, Canada: Vancouver, pp. 4013–4020.

  14. Muneesawang, P., Wong, H. S., Lay, J., & Guan, L. (2002). Learning and adaptive characterization of visual contents in image retrieval systems. Handbook of neural network for signal processing. Boca Raton: CRC Press.

    Google Scholar 

  15. Mori, Y., Takahashi, H., Oka, R. (1999). Image-to-word transformation based on dividing and vector quantizing images with words. Proc. Int. Workshop on Multimedia Intelligent Storage and Retrieval Management.

  16. Duygulu, P., Barnard, K., Freitas, N., Forsyth, D. (2002). Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. Proc. European Conf. Computer Vision, pp. 97–112.

  17. Jeon, J., Lavrenko, V., Manmatha, R. (2003). Automatic image annotation and retrieval using cross-media relevance models. Proc. Int. ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 119–126.

  18. Monay, F., Gatica-Perez, D. (2003). On image auto-annotation with latent space models. Proc. ACM Int. Conf. Multimedia, pp. 275–278.

  19. Blei, D., Ng, A., & Jordan, M. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. doi:10.1162/jmlr.2003.3.4-5.993.

    Article  MATH  Google Scholar 

  20. Blei, D., Jordan, M. (2003). Modeling annotated data. Proc. Int. ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 127–134.

  21. Chang, E., Kingshy, G., Sychay, G., & Wu, G. (2003). CBSA: Content-based soft annotation for multimodal image retrieval using bayes point machines. IEEE Transactions on Circuits and Systems for Video Technology, 13(1), 26–38. doi:10.1109/TCSVT.2002.808079.

    Article  Google Scholar 

  22. Li, J., & Wang, J. Z. (2003). Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9), 1075–1088. doi:10.1109/TPAMI.2003.1227984.

    Article  Google Scholar 

  23. Goh, K., & Chang, E. (2005). Using one-class and two-class SVMs for multiclass image annotation. IEEE Transactions on Knowledge and Data Engineering, 17(10), 1333–1346. doi:10.1109/TKDE.2005.170.

    Article  Google Scholar 

  24. Begelman, G., Keller, P., Smadja, F. (2006). Automated tag clustering: improving search and exploration in the tag space. 15th International World Wide Web Conference, Edinburgh, UK.

  25. Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619. doi:10.1109/34.1000236.

    Article  Google Scholar 

  26. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.

    Google Scholar 

  27. Mitchell, M. (1996). An Introduction to Genetic Algorithms. Cambridge: MIT Press.

    Google Scholar 

  28. Findlay, J. (1980). The visual stimulus for saccadic eye movement in human observers. Perception, 9, 7–21. doi:10.1068/p090007.

    Article  Google Scholar 

  29. Senders, J. (1997). Distribution of attention in static and dynamic scenes. Proc. SPIE, 3016, 186–194. doi:10.1117/12.274513.

    Article  Google Scholar 

  30. Yarbus, A. (1967). Eye Movements and Vision. New York: Plenum Press.

    Google Scholar 

  31. Elias, G., Sherwin, G., Wise, J. (1984). Eye movements while viewing NTSC format television. SMPTE Psychophysics Subcommittee white paper.

  32. Baker, J. E. (1987). Reducing bias and inefficiency in the selection algorithm. Proceedings of the 2 nd International Conference on Genetic Algorithms, pp.14–21.

  33. Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., Williamson, R. C. (1999). Estimating the support of a high-dimensional distribution. Technical report MSR-TR-99-87, Microsoft.

  34. Haykin, S. (1999). Neural Networks a Comprehensive Foundation. Upper Saddle River: Prentice-Hall.

    MATH  Google Scholar 

  35. Rubner, Y., Tomasi, C., & Guibas, L. (2000). The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision, 40, 99–123. doi:10.1023/A:1026543900054.

    Article  MATH  Google Scholar 

  36. Chiu, S. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems, 2(3), 267–278.

    Google Scholar 

  37. Markus, S., Markus, O. (1995). Similarity of color images. Proc. SPIE Storage and Retrieval for Image and Video Databases, pp. 381–392.

  38. Smith, J. R., Chang, S. F. (1996). Automated binary texture feature sets for image retrieval. Proc. Int. Conf. Acoustics, Speech, and Signal Processing, Atlanta, GA, pp. 2239–2242.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore

    Kim-Hui Yap, Kui Wu & Ce Zhu

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  1. Kim-Hui Yap
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  2. Kui Wu
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  3. Ce Zhu
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Correspondence to Kui Wu.

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Yap, KH., Wu, K. & Zhu, C. Knowledge Propagation in Collaborative Tagging for Image Retrieval. J Sign Process Syst Sign Image Video Technol 59, 163–175 (2010). https://doi.org/10.1007/s11265-008-0288-1

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  • DOI: https://doi.org/10.1007/s11265-008-0288-1

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