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Relevance Feedback in Content-Based Image Retrieval: A Survey

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Handbook on Neural Information Processing

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 49))

Abstract

In content-based image retrieval, relevance feedback is an interactive process, which builds a bridge to connect users with a search engine. It leads to much improved retrieval performance by updating a query and similarity measures according to a user’s preference; and recently techniques have matured to some extent. Most previous relevance feedback approaches exploit short-term learning (intraquery learning) that deals with the current feedback session but ignoring historical data from other users, which potentially results in a great loss of useful information. In the last few years, long-term learning (inter-query learning), by recording and collecting feedback knowledge from different users over a variety of query sessions has played an increasingly important role in multimedia information searching. It can further improve the retrieval performance in terms of effectiveness and efficiency. In the published literature, no comprehensive survey of both short-term learning and long-term learning RF techniques has been conducted. To this end, the goal of this chapter is to address this omission and offer suggestions for future work.

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References

  1. Anick, P.: Using Terminological Feedback for Web Search Refinement - A Log-based Study. In: Proc. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 88–95 (2003)

    Google Scholar 

  2. Bian, W., Tao, D.: Biased Discriminant Euclidean Embedding for Content-Based Image Retrieval. IEEE Transactions on Image Processing 19(2), 545–554 (2010)

    Article  MathSciNet  Google Scholar 

  3. Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Cotraining. In: COLT: Proc. Workshop on Computational Learning Theory (1998)

    Google Scholar 

  4. Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1995)

    Google Scholar 

  5. Burges, J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)

    Article  Google Scholar 

  6. Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying. IEEE Trans. Pattern Analysis and Machine Intelligence 24(8), 1026–1038 (2002)

    Article  Google Scholar 

  7. Chang, T., Kuo, C.: Texture Analysis and Classification with Tree-Structured Wavelet Transform. IEEE Trans. Image Processing 2(4), 429–441 (1993)

    Article  Google Scholar 

  8. Chang, H., Yeung, D.Y.: Stepwise Metric Adaptation Based on Semi-Supervised Learning for Boosting Image Retrieval Performance. In: British Machine Vision Conference (2005)

    Google Scholar 

  9. Chen, X., Zhang, C., Chen, S.C., Chen, M.: A Latent Semantic Indexing Based Method for Solving Multiple Instance Learning Problem in Region-Based Image Retrieval. In: Proc. IEEE Int’l Symposium Multimedia, pp. 37–45 (2005)

    Google Scholar 

  10. Chen, Y., Zhou, X., Huang, T.S.: One-class SVM for Learning in Image Retrieval. In: Proc. IEEE Int’l Conf. Image Processing, vol. 1, pp. 34–37 (2001)

    Google Scholar 

  11. Cord, M., Gosselin, P.H.: Image Retrieval using Long-Term Semantic Learning. In: IEEE Int’l Conf. Image Processing, pp. 2,909–2,912 (2006)

    Google Scholar 

  12. Cox, I.J., Miller, M.L., Minka, T.P., Papathomas, T.V., Yianilos, P.N.: The Bayesian Image Retrieval System, PicHunter: Theory, Implementation and Psychophysical Experiments. IEEE Trans. Image Processing 9(1), 20–37 (2000)

    Article  Google Scholar 

  13. Cui, H., Wen, J., Nie, J., Ma, W.: Query Expansion by Mining User Logs. IEEE Trans. Knowledge and Data Engineering 15(4), 829–939 (2003)

    Article  Google Scholar 

  14. Daubechies, I.: The Wavelet Transform, Time-frequency Localization and Signal Analysis. IEEE Trans. Information Theory 36(5), 961–1005 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  15. Daugman, J.G.: Two-Dimensional Spectral Analysis of Cortical Receptive Field Profile. Vision Research 20, 847–856 (1980)

    Article  Google Scholar 

  16. Daugman, J.G.: Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two–Dimensional Visual Cortical Filters. J. Optical Society of America 2(7), 1,160–1,169 (1985)

    Google Scholar 

  17. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. American Society for Information Science 41, 391–407 (1990)

    Article  Google Scholar 

  18. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: ImageNet: A Large-scale Hierarchical Image Database. In: IEEE Conf. Computer Vision and Pattern Recognition (2009)

    Google Scholar 

  19. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, John & Sons, Incorporated (1999)

    Google Scholar 

  20. Fahlman, S.E., Lebiere, C.: The Cascade-Correlation Learning Architecture. In: Advances in Neural Information Processing Systems 2, pp. 524–532 (1990)

    Google Scholar 

  21. Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: Proc. Int’l Conf. Machine Learning, pp. 148–156 (1996)

    Google Scholar 

  22. Fournier, J., Cord, M.: Long-term Similarity Learning in Content-based Image Retrieval. In: Proc. IEEE Int’l Conf. Image Processing, vol. 1, pp. 1,441–1,444 (2002)

    Google Scholar 

  23. Gevers, T., Smeulders, A.: Pictoseek: Combining color and shape invariant features for image retrieval. IEEE Trans. Image Processing 9(1), 102–119 (2000)

    Article  Google Scholar 

  24. Giacinto, G., Roli, F.: Instance-Based Relevance Feedback for Image Retrieval. In: Advances Neural Information Processing Systems, pp. 489–496 (2004)

    Google Scholar 

  25. Gosselin, P.H., Cord, M.: Semantic Kernel Learning for Interactive Image Retrieval. In: IEEE Int’l Conf. Image Processing, vol. 1, 1,177–1,180 (2005)

    Google Scholar 

  26. Guo, G., Jain, A.K., Ma, W., Zhang, H.: Learning Similarity Measure for Natural Image Retrieval with Relevance Feedback. IEEE Trans. Neural Networks 12(4), 811–820 (2002)

    Google Scholar 

  27. Han, J., Ngan, K.N., Li, M., Zhang, H.J.: A Memory Learning Framework for Effective Image Retrieval. IEEE Trans. Image Processing 14(4), 511–524 (2005)

    Article  Google Scholar 

  28. He, X., King, O., Ma, W., Li, M., Zhang, H.: Learning a Semantic Space from User’s Relevance Feedback for Image Retrieval. IEEE Trans. Circuits and Systems for Video Technology 13(1), 39–48 (2003)

    Article  Google Scholar 

  29. He, X., Ma, W.Y., Zhang, H.J.: Learning an Image Manifold for Retrieval. In: Proc. ACM Int’l Conf. Multimedia, pp. 17–23 (2004)

    Google Scholar 

  30. Heisterkamp, D.R.: Building a Latent Semantic Index of an Image Database from Patterns of Relevance Feedback. In: Proc. Int’l Conf. Pattern Recognition, vol. 4, pp. 134–137 (2002)

    Google Scholar 

  31. Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Trans. Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)

    Article  Google Scholar 

  32. Hoi, C.H., Chan, C.H., Huang, K., Lyu, M.R., King, I.: Biased Support Vector Machine for Relevance Feedback in Image Retrieval. In: Proc. Int’l Joint Conf. Neural Networks, pp. 3189–3194 (2004)

    Google Scholar 

  33. Hoi, C.H., Lyu, M.R.: Group-based Relevance Feedback with Support Vector Machine Ensembles. In: Int’l Conf. Pattern Recognition, vol. 3, pp. 874–877 (2004)

    Google Scholar 

  34. Hoi, S.C.H., Lyu, M.R., Jin, R.: A Unified Log-based Relevance Feedback Scheme for Image Retrieval. IEEE Trans. Knowledge and Data Engineering 18(4), 509–524 (2006)

    Article  Google Scholar 

  35. Hsu, C.T., Li, C.Y.: Relevance Feedback Using Generalized Bayesian Framework With Region-Based Optimization Learning. IEEE Trans. Image Processing 14(10), 1,617–1,631 (2005)

    Google Scholar 

  36. Jain, A., Vailaya, A.: Image Retrieval Using Color and Shape. Pattern Recognition 29(8), 1233–1244 (1996)

    Article  Google Scholar 

  37. Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall Inc., New Jersey (1989)

    MATH  Google Scholar 

  38. Jain, A.K., Lee, J.E., Jin, R., Gregg, N.: Content-based Image Retrieval: An Application to Tattoo Images. In: IEEE Int’l Conf. Image Processing, vol. 2, pp. 745–742 (2009)

    Google Scholar 

  39. Jiang, W., Er, G., Dai, Q., Gu, J.: Hidden Annotation for Image Retrieval with Long-Term Relevance Feedback Learning. Pattern Recognition 38(11), 2,007–2,021 (2005)

    Google Scholar 

  40. Jing, F., Li, M., Zhang, H., Zhang, B.: Relevance Feedback in Region-Based Image Retrieval. IEEE Trans. Circuits and Systems for Video Technology 14(5), 672–681 (2004)

    Article  Google Scholar 

  41. Koskela, M., Laaksonen, J.: Using Long-Term Learning to Improve Efficiency of Content-Based Image Retrieval. In: Proc. Int’l Workshop on Pattern Recognition in Information Systems, pp. 72–79 (2003)

    Google Scholar 

  42. Kushki, A., Androutsos, P., Plataniotis, K.N., Venetsanopoulos, A.N.: Query Feedback for Interactive Image Retrieval. IEEE Trans. Circuits and Systems for Video Technology 14, 644–655 (2004)

    Article  Google Scholar 

  43. Laaksonen, J., Koskela, M., Laakso, S., Oja, E.: PicSOM—Content-based Image Retrieval with Self-Organizing Maps. Pattern Recognition Letters 21(13-14), 1,199–1,207 (2000)

    Google Scholar 

  44. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation 1(4), 541–551 (1989)

    Article  Google Scholar 

  45. Leung, A.P., Auer, P.: An Efficient Search Algorithm for Content-Based Image Retrieval with User Feedback. In: IEEE Int’l Conf. Data Mining Workshops, pp. 884–890 (2008)

    Google Scholar 

  46. Li, J., Allinson, N.M.: A Comprehensive Review of Current Local Features for Computer Vision. Neurocomputing 71(10-12), 1771–1787 (2008)

    Article  Google Scholar 

  47. Li, J., Allinson, N.M.: Long-term Learning in Content-based Image Retrieval. Int’l J. Imaging Systems and Technology 18(2-3), 160–169 (2008)

    Article  Google Scholar 

  48. Li, J., Allinson, N., Tao, D., Li, X.: Multitraining Support Vector Machine for Image Retrieval. IEEE Trans. Image Processing 15(11), 3,597–3,601 (2006)

    Google Scholar 

  49. Long, F., Zhang, H., Feng, D.: Fundamentals of Content-based Image Retrieval. In: Multimedia Information Retrieval and Management, pp. 1–12. Springer (2003)

    Google Scholar 

  50. Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int’l J. Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  51. Lu, Y., Zhang, H., Liu, W., Hu, C.: Joint Semantics and Feature Based Image Retrieval Using Relevance Feedback. IEEE Trans. Multimedia 5(3), 339–347 (2003)

    Article  Google Scholar 

  52. Manjunath, B., Ma, W.: Texture Features for Browsing and Retrieval of Image Data. IEEE Trans. Pattern Analysis and Machine Intelligence 18(8), 837–842 (1996)

    Article  Google Scholar 

  53. Manjunath, B., Ohm, J., Vasudevan, V., Yamada, A.: Color and Texture Descriptors. IEEE Trans. Circuits and Systems for Video Technology 11(6), 703–715 (2001)

    Article  Google Scholar 

  54. Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. IEEE Trans. Pattern Analysis and Machine Intelligence 27(10), 1,615–1,630 (2005)

    Google Scholar 

  55. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A Comparison of Affine Region Detectors. Int’l J. Computer Vision 65(1/2), 43–72 (2005)

    Article  Google Scholar 

  56. Minka, T.P., Picard, R.W.: Interactive Learning with a “Society of Models”. Pattern Recognition 30(4), 565–581 (1997)

    Article  Google Scholar 

  57. Müller, H., Müller, W., Squire, D.M., Marchand-Maillet, S., Pun, T.: Long-Term Learning from User Behavior in Content-Based Image Retrieval. Technical report, University of Geneva (2000)

    Google Scholar 

  58. Nakazato, M., Dagli, C., Huang, T.S.: Evaluating Group-based Relevance Feedback for Content-based Image Retrieval. In: Proc. IEEE Int’l. Conf. on Image Processing, pp. 599–602 (2003)

    Google Scholar 

  59. Nedovic, V., Marques, O.: A Collaborative, Long-term Learning Approach to Using Relevance Feedback in Content-based Image Retrieval Systems. In: Int’l Symposium ELMAR, pp. 143–146 (2005)

    Google Scholar 

  60. Niblack, W., Barber, R., Equitz, W., Flickner, M., Glasman, E., Petkovic, D., Yanker, P., Faloutsos, C., Taubin, G.: The QBIC project: Quering Images by Content using Color, Texture, and Shape. In: Proc. SPIE Storage and Retrieval for Image and Video Databases, pp. 173–187 (1993)

    Google Scholar 

  61. Ortega, M., Rui, Y., Chakrabarti, K., Mehrotra, S., Huang, T.S.: Supporting similarity queries in MARS. In: Proc. ACM Int’l Conf. Multimedia, pp. 403–413 (1997)

    Google Scholar 

  62. Pass, G., Zabih, R., Miller, J.: Comparing Images Using Color Coherence Vectors. In: Proc. ACM Int’l Conf. Multimedia, pp. 65–73 (1996)

    Google Scholar 

  63. Rocchio, J.J.: Document Retrieval System: Optimization and Evaluation. PhD dissertation, Harvard Computational Lab, Harvard University, Cambridge, MA (1996)

    Google Scholar 

  64. Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover’s Distance as a Metric for Image Retrieval. Int’l J. Computer Vision 40(2), 99–121 (2000)

    Article  MATH  Google Scholar 

  65. Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance Feedback: A Power Tool in Interactive Content-based Image Retrieval. IEEE Trans. Circuits and Systems for Video Technology 8(5), 644–655 (1998)

    Article  Google Scholar 

  66. Sav, S., O’Connor, N., Smeaton, A., Murphy, N.: Associating Low-level Features with Semantic Concepts using Video Objects and Relevance Feedback. In: Int’l Workshop on Image Analysis for Multimedia Interactive Services (2005)

    Google Scholar 

  67. Schapire, R.E.: The Strength of Weak Learnability. Machine Learning 5(2), 197–227 (1990)

    Google Scholar 

  68. Schmid, C., Mohr, R.: Local Grayvalue Invariants for Image Retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 19(5), 530–534 (1997)

    Article  Google Scholar 

  69. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Analysis and Machine Intelligence 22(12), 1,349–1,380 (2000)

    Google Scholar 

  70. Su, Z., Li, S., Zhang, H.: Extraction of Feature Subspaces for Content-Based Retrieval Using Relevance Feedback. In: Proc. of ACM Multimedia, pp. 98–106 (2001)

    Google Scholar 

  71. Su, Z., Zhang, H., Li, S., Ma, S.: Relevance Feedback in Content-Based Image Retrieval: Bayesian Framework, Feature Subspaces, and Progressive Learning. IEEE Trans. Image Processing 12(8), 924–937 (2003)

    Article  Google Scholar 

  72. Swain, M., Ballard, D.: Color Indexing. Int’l J. Computer Vision 7(1), 11–32 (1991)

    Article  Google Scholar 

  73. Tai, X., Ren, F., Kita, K.: Long-Term Relevance Feedback Using Simple PCA and Linear Transformation. In: Proc. Int’l Workshop on Database and Expert Systems Applications, pp. 261–268 (2002)

    Google Scholar 

  74. Tamura, H., Mori, S., Yamawaki, T.: Texture Features Corresponding to Visual Perception. IEEE Trans. Systems, Man, and Cybernetics 8(6), 460–473 (1978)

    Article  Google Scholar 

  75. Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric Bagging and Random Subspace for Support Vector Machines-based Relevance Feedback in Image Retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 28(7), 1,088–1,099 (2006)

    Google Scholar 

  76. Tao, D., Tang, X., Li, X., Rui, Y.: Kernel Direct Biased Discriminant Analysis: A New Content-based Image Retrieval Relevance Feedback Algorithm. IEEE Trans. Multimedia 8(4), 716–727 (2006)

    Article  MATH  Google Scholar 

  77. Tao, D., Li, X., Maybank, S.J.: Negative Samples Analysis in Relevance Feedback. IEEE Trans. Knowledge and Data Engineering 19(4), 568–580 (2007)

    Article  Google Scholar 

  78. Tong, S., Chang, E.: Support Vector Machine Active Learning for Image Retrieval. In: Proc. ACM Int’l Conf. Multimedia, pp. 107–118 (2001)

    Google Scholar 

  79. Torralba, A., Fergus, R., Freeman, W.T.: 80 Million Tiny Images: A Large Dataset for Non-parametric Object and Scene Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 30(11), 1958–1970 (2008)

    Article  Google Scholar 

  80. Torre, F.D.L., Kanade, T.: Multimodal Oriented Discriminant Analysis. In: Int’l Conf. Machine Learning (2005)

    Google Scholar 

  81. Tversky, A.: Features of Similarity. Psychological Review 84(4), 327–352 (1977)

    Article  Google Scholar 

  82. Urban, J., Jose, J.M.: Adaptive Image Retrieval using a Graph Model for Semantic Feature Integration. In: Proc. ACM Int’l Workshop on Multimedia Information Retrieval, pp. 117–126 (2006)

    Google Scholar 

  83. Vapnik, V.: The Nature of Statistical Learning Theory. Springer (1995)

    Google Scholar 

  84. Vasconcelos, N., Kunt, M.: Content-based Retrieval from Image Databases: Current Solutions and Future Directions. In: Proc. IEEE Int’l Conf. Image Processing, vol. 3, pp. 6–9 (2001)

    Google Scholar 

  85. Wacht, M., Shan, J., Qi, X.: A Short-Term and Long-Term Learning Approach for Content-Based Image Retrieval. In: Proc. IEEE Int’l Conf. Acoustics, Speech, and Signal Processing, vol. 2, pp. 389–392 (2006)

    Google Scholar 

  86. Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries. IEEE Trans. Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001)

    Article  Google Scholar 

  87. Yang, J., Li, Q., Zhuang, Y.: Image Retrieval and Relevance Feedback using Peer Indexing. In: Proc. IEEE Int’l Conf. Multimedia and Expo, vol. 2, pp. 409–412 (2002)

    Google Scholar 

  88. Yin, P.Y., Bhanu, B., Chang, K.C., Dong, A.: Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning. IEEE Trans. Pattern Analysis and Machine Intelligence 27(10), 1,536–1,551 (2005)

    Google Scholar 

  89. Yoshizawa, T., Schweitzer, H.: Long-term Learning of Semantic Grouping from Relevance-feedback. In: Proc. ACM SIGMM Int’l Workshop on Multimedia Information Retrieval, pp. 165–172 (2004)

    Google Scholar 

  90. Zhang, L., Lin, F., Zhang, B.: Support Vector Machine Learning for Image Retrieval. In: Proc. IEEE Int. Conf. Image Processing, vol. 2, pp. 21–724 (2001)

    Google Scholar 

  91. Zhou, X.S., Huang, T.S.: Comparing Discriminating Transformations and SVM for Learning during Multimedia Retrieval. In: Proc. ACM International Conference on Multimedia (2001)

    Google Scholar 

  92. Zhou, X.S., Huang, T.S.: Relevance feedback in image retrieval: A comprehensive review. Multimedia Systems 8(6), 536–544 (2003)

    Article  Google Scholar 

  93. Zhou, X.S., Huang, T.S.: Small Sample Learning during Multimedia Retrieval using Biasmap. In: Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 11–17 (2001)

    Google Scholar 

  94. Zhou, X.D., Zhang, L., Liu, L., Zhang, Q., Shi, B.: A Relevance Feedback Method in Image Retrieval by Analyzing Feedback Log File. In: Proc. Int’l Conf. Machine Learning and Cybernetics, vol. 3, pp. 1,641–1,646 (2002)

    Google Scholar 

  95. Zhou, Z.H.: Ensemble Learning. In: Encyclopedia of Biometrics, pp. 270–273 (2009)

    Google Scholar 

  96. Zhuang, Y., Yang, J., Li, Q., Pan, Y.: A Graphic-theoretic Model for Incremental Relevance Feedback in Image Retrieval. In: Proc. Int’l Conf. Image Processing, vol. 1, pp. 413–416 (2002)

    Google Scholar 

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  1. School of Information Engineering, Nanchang University, Nanchang, Peoples Republic of China

    Jing Li

  2. School of Computer Science, University of Lincoln, Lincoln, UK

    Nigel M. Allinson

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  1. Jing Li
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  1. , Dipto. Ingegneria dell'Informazione, Università degli Studi di Siena, Via Roma 56, Siena, 53100, Italy

    Monica Bianchini

  2. Fac. Ingegneria, Dipto. Ingegneria dell'Informazione, Università Siena, Via Roma 56, Siena, 53100, Italy

    Marco Maggini

  3. University of Canberra, School of Electrical and Information, Adjunct Professor, Mawson Lakes Campus, ACT, 2601, Australia

    Lakhmi C. Jain

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Li, J., Allinson, N.M. (2013). Relevance Feedback in Content-Based Image Retrieval: A Survey. In: Bianchini, M., Maggini, M., Jain, L. (eds) Handbook on Neural Information Processing. Intelligent Systems Reference Library, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36657-4_13

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