Abstract
Understanding and learning the subjective aspect of humans in Content-Based Image Retrieval has been an active research field during the past few years. However, how to effectively discover users’ concept patterns when there are multiple visual features existing in the retrieval system still remains a big issue. In this book chapter, we propose a multimedia data mining framework that incorporates Multiple Instance Learning into the user relevance feedback in a seamless way to discover the concept patterns of users, especially where the user’s most interested region and how to map the local feature vector of that region to the high-level concept pattern of users. This underlying mapping can be progressively discovered through the feedback and learning procedure. The role the user plays in the retrieval system is to guide the system mining process to his/her own focus of attention. The retrieval performance is tested to show the feasibility and effectiveness of the proposed multimedia data mining framework.
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References
Aksoy, S., Haralick, R.M.: A Weighted Distance Approach to Relevance Feedback. In: Proceedings of the International Conference on Pattern Recognition, pp. 812–815 (2000)
Auer, P.: On Learning From Multi-instance Examples: Empirical Evaluation of a Theoretical Approach. In: Proceedings of 14th International Conference on Machine Learning, San Francisco, CA, pp. 21–29 (1997)
Buckley, C., Singhal, A., Miltra, M.: New Retrieval Approaches Using SMART: TREC4. In: Text Retrieval Conference, Sponsored by National Institute of Standard and Technology and Advanced Research Projects Agency, pp. 25–48 (1995)
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1026–1038 (2002)
Chang, C.-H., Hsu, C.-C.: Enabling Concept-Based Relevance Feedback for Information Retrieval on the WWW. IEEE Transactions on Knowledge and Data Engineering 11, 595–609 (1999)
Cox, I.J., Minka, T.P., Papathomas, T.V., Yianilos, P.N.: The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments. IEEE Transactions on Image Processing–special issue on digital libraries 9(1), 20–37 (2000)
Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the Multiple- Instance Problem with Axis-Parallel Rectangles. Artificial Intelligence Journal 89, 31–71 (1997)
Ishikawa, Y., Subramanya R., Faloutsos, C.: Mindreader: Query Databases Through Multiple Examples. In: Proceedings of the 24th International Conference on Very Large Databases, (1998)
Marks II, R.J., Oh, S., Arabshahi, P., Caudell, T.P., Choi, J.J., Song, B.G.: Steepest Descent Adaptation of Min-Max Fuzzy If-Then Rules. In: Proceedings of the IEEE/INNS International Conference on Neural Networks, Beijing, China, vol. 3, pp. 471–477 (1992)
Maron, O., Lozano-Perez, T.: Multiple-Instance A Framework for Multiple- Instance Learning. In: Advances in Neural Information Processing System 10, MIT Press. Cambridge (1998)
Nagasska, A., Tanaka, Y.: Automatic Video Indexing and Full Video Search for Object Appearance. IFIP Trans. Visual Database Systems II, 113–127 (1992)
Ramon, J., De Raedt, L.: Multi-Instance Neural Networks. In: Proceedings of the ICML 2000 Workshop on Attribute-value and Relational Learning (2000)
Ray, S., Page, D.: Multiple-Instance Regression. In: Proceedings of the 18th International Conference on Machine Learning, San Francisco, CA, pp. 425–432 (2001)
Rocchio, J.J.: Relevance Feedback in Information Retrieval. In: The Smart System experiments in automatic document processing, pp. 313–323. Prentice Hall Inc., Englewood Cliffs (1971)
Rui, Y., Huang, T.S., Mehrotra, S.: Content-based image retrieval with relevance feedback in MARS. In: Proceedings of the, International Conference on Image Processing, pp. 815–818 (1997)
Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance Feedback: A Power Tool in Interactive Content-Based Image Retrieval. IEEE Transaction on Circuits and Systems for Video Technology, Special Issue on Segmentation, Description, and Retrieval of Video Content 18(5), 644–655 (1998)
Rui, Y., Huang, T.S.: Optimizing Learnin In Image Retrieval. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 236–243 (2000)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill Book Company, New York (1983)
Wang, J., Zucker, J.-D.: Solving the Multiple-Instance Learning Problem: A Lazy Learning Approach. In: Proceedings of the 17th International Conference on Machine Learning, pp. 1119–1125 (2000)
Yang, C., Lozano-Prez, T.: Image Database Retrieval with Multiple-Instance Learning Techniques. In: Proceedings of the 16th International Conference on Data Engineering, pp. 233–243 (2000)
Zhang, Q., Goldman, S.A.: EM-DD: An Improved Multiple-Instance Learning Technique. In: Advances in Neural Information Processing Systems, NIPS 2002 (2002) (to be published)
Zhang, Q., Goldman, S.A., Yu, W., Fritts, J.: Content-Based Image Retrieval Using Multiple-Instance Learning. In: Proceedings of the 9th International Conference on Machine Learning (2002)
Zucker, J.-D., Chevaleyre, Y.: Solving Multiple-instance and Multiple-part Learning Problems with Decision Trees and Decision Rules. Application to the Mutagenesis Problem. In: Proceedings of the 14th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2001, pp. 204–214 (2001)
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Huang, X., Chen, SC., Shyu, ML., Zhang, C. (2003). Mining High-Level User Concepts with Multiple Instance Learning and Relevance Feedback for Content-Based Image Retrieval. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds) Mining Multimedia and Complex Data. PAKDD 2002. Lecture Notes in Computer Science(), vol 2797. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39666-6_4
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DOI: https://doi.org/10.1007/978-3-540-39666-6_4
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