Support vector machine concept-dependent active learning for image retrieval
Inhaltsverzeichnis
Referenz
E. Chang, S. Tong, K. Goh, and C. Chang: Support vector machine concept-dependent active learning for image retrieval. IEEE Trans. on Multimedia, 2, 2005.
DOI
Abstract
Relevance feedback is a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively learns a user’s desired output or query concept by asking the user whether certain proposed images are relevant or not. For a learning algorithm to be effective, it must learn a user’s query concept accurately and quickly, while also asking the user to label only a small number of images. In addition, the concept-learning algorithm should consider the complexity a concept in determining its learning strategies. In this paper, we present the use of support vector machines active learning in a concept-dependent way (SVMCD Active ) for conducting relevance feedback. We characterize a concept’s complexity using three measures: hit-rate, isolation and diversity. To reduce concept complexity so as to improve concept learnability, we propose a multimodal learning approach that uses images’ semantic labels to intelligently adjust the sampling strategy and the sampling pool of SVMCD Active . Our empirical study on several datasets shows that active learning outperforms traditional passive learning, and concept-dependent learning is superior to the traditional concept- independent relevance-feedback schemes.
Extended Abstract
Bibtex
@article{
author = {E. Chang, S. Tong, K. Goh, and C. Chang},
title = {Support vector machine concept-dependent active learning for image retrieval},
journal = {IEEE Trans. on Multimedia},
volume = {},
number = {},
pages = {},
year = {2005},
doi={},
url={http://www.robotics.stanford.edu/~stong/papers/chang-tong-goh-chang-ieeemm.pdf http://de.evo-art.org/index.php?title=Support_vector_machine_concept-dependent_active_learning_for_image_retrieval},
}
Used References
[1] R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. Proceedings of ACM SIGMOD, 1993.
[2] C. Bishop. Neural Networks for Pattern Recognition. Oxford, 1998.
[3] L. Breiman. Bagging predicators. Machine Learning, pages 123–140, 1996.
[4] L. Breiman. Arcing classifiers. The Annals of Statistics, pages 801–849, 1998.
[5] K. Brinker. Incorporating diversity in active learning with support vector machines. Proceedings of the Twentieth International Conference on Machine Learning, pages 59–66, August 2003.
[6] C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2:121–167, 1998.
[7] C. Campbell, N. Cristianini, and A. Smola. Query learning with large margin classifiers. In Proceedings of the Seventeenth International Conference on Machine Learning, 2000.
[8] N. Cesa-Bianchi, Y. Freund, D. Haussler, D. P. Helmbold, R. E. Schapire, and M. K. Warmuth. How to use expert advice. Journal of ACM, 44(3):427–485, 1997.
[9] E. Chang, K. Goh, G. Sychay, and G. Wu. Content-based soft annotation for multimodal image retrieval using bayes point machines. IEEE Transactions on Circuits and Systems for Video Technology Special Issue on Conceptual and Dynamical Aspects of Multimedia Content Description, 13(1):26–38, 2003.
[10] E. Chang and B. Li. Mega — the maximizing expected generalization algorithm for learning complex query concepts. ACM Transaction on Information Systems, December 2003.
[11] E. Y. Chang, B. Li, G. Wu, and K.-S. Goh. Statistical learning for effective visual information retrieval (invited paper). IEEE International Conference on Image Processing (ICIP), pages 609–612, 2003.
[12] D. Cohn, Z. Ghahramani, and M. Jordan. Active learning with statistical models. Journal of Artificial Intelligence Research, 4, 1996.
[13] I. Cox, J. Ghosn, M. Miller, T. Papathomas, and P. Yianilos. Introduces pichunter, the bayesian framework, and describes a working system including measured user performance. hidden annotation in content based image retrieval. In IEEE Workshop on Content-Based Access of Image & Video Libraries, pages 76–81, June 1997.
[14] I. Cox, M. Miller, S. Omohundo, and P. Yianilos. Pichunter: Bayesian relevance feedback for image retrieval. In 13th International Conference on Pattern Recognition, pages 361–369, August 1996.
[15] I. Cox, M. Miller, S. Omohundo, and P. Yianilos. Target testing and the pichunter bayesian multimedia retrieval system. In Proceedings of the Forum on Research & Technology Advances in Digital Libraries, pages 66–75, 1996.
[16] I. J. Cox, M. L. Miller, T. P. Minka, T. V. Papathomas, and P. N. Yianilos. The bayesian image retrieval system, pichunter: Theory, implementation and psychological experiments. IEEE Transaction on Image Processing, 2000.
[17] I. Dagan and S. Engelson. Committee-based sampling for training probabilistic classifiers. In Proceedings of the Twelfth International Conference on Machine Learning, pages 150–157. Morgan Kaufmann, 1995.
[18] T. Dietterich and G. Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artifical Intelligence Research, 2, 1995.
[19] R. Duda, P. Hart, and D. G. Stork. Pattern Classification. Wiley, New York, 2 edition, 2001.
[20] S. Dumais, J. Platt, D. Heckerman, and M. Sahami. Inductive learning algorithms and representations for text categorization. In Proceedings of the Seventh International Conference on Information and Knowledge Management. ACM Press, 1998.
[21] M. Flickner, H. Sawhney, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker. Query by image and video content: the QBIC system. IEEE Computer, 28(9):23–32, 1995.
[22] Y. Freund, H. Seung, E. Shamir, and N. Tishby. Selective sampling using the Query by Committee algorithm. Machine Learning, 28:133–168, 1997.
[23] P. Gosselin and M. Cord. Retin al: An active learning strategy for image category retrieval. IEEE ICIP, 2004.
[24] A. Grove and D. Schuurmans. Boosting in the limit: Maximizing the margin of learned ensembles. In Proc. 15th National Conference on Artificial Intelligence (AAAI), 1998.
[25] A. Gupta and R. Jain. Visual information retrieval. Comm. of the ACM, 40(5):69–79, 1997.
[26] R. Herbrich, T. Graepel, and C. Campbell. Bayes point machines: Estimating the bayes point in kernel space. In International Joint Conference on Artificial Intelligence Workshop on Support Vector Machines, pages 23–27, 1999.
[27] K. A. Hua, K. Vu, and J.-H. Oh. Sammatch: A flexible and efficient sampling-based image retrieval technique for image databases. Proceedings of ACM Multimedia, November 1999.
[28] Y. Ishikawa, R. Subramanya, and C. Faloutsos. Mindreader: Querying databases through multiple exam- ples. VLDB, 1998.
[29] T. Jaakkola and H. Siegelmann. Active information retrieval. Advances in Neural Information processing systems 14, 2001.
[30] G. James and T. Hastie. Error Coding and PaCT’s. 1997.
[31] T. Joachims. Text categorization with support vector machines. In Proceedings of the European Conference on Machine Learning. Springer-Verlag, 1998.
[32] K. S. Jones and P. W. (Editors). Readings in Information Retrieval. Morgan Kaufman, July 1997.
[33] M. Kearns and U. Vazirani. An Introduction to Computational Learning Theory. MIT Press, 1994.
[34] W.-C. Lai, K. Goh, and E. Y. Chang. On scalability of active learning for formulating query concepts. Workshop on Computer Vision Meets Databases (CVDB) in cooperation with ACM International Confer- ence on Management of Data (SIGMOD), pages 11–18, 2004.
[35] D. Lewis andW. Gale. A sequential algorithm for training text classifiers. In Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, pages 3–12. Springer-Verlag, 1994.
[36] C. Li, E. Chang, H. Garcia-Molina, and G. Wilderhold. Clindex: Approximate similarity queries in high- dimensional spaces. IEEE Transactions on Knowledge and Data Engineering (TKDE), 14(4), July 2002.
[37] M. Lindenbaum, S. Markovitch, and D. Rusakov. Selective sampling for nearest neighbor classifiers. Mach. Learn., 54(2):125–152, 2004.
[38] B. S. Manjunath and W. Y. Ma. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8):837–842, August 1996.
[39] A. McCallum and K. Nigam. Employing EM in pool-based active learning for text classification. In Proceedings of the Fifteenth International Conference on Machine Learning. Morgan Kaufmann, 1998.
[40] T. Mitchell. Generalization as search. Artificial Intelligence, 28:203–226, 1982.
[41] T. M. Mitchell. Machine Learning. McGraw-Hill, 1997.
[42] M. Moreira and E. Mayoraz. Improving pairwise coupling classification with error correcting classifiers. Proceedings of the Tenth European Conference on Machine Learning, April 1998.
[43] H. Nguyen and A. W. Smeulders. Active learning using pre-clustering. Proc. of the 21th Inter. Conf. on Machine Learning, 2004.
[44] M. Ortega, Y. Rui, K. Chakrabarti, S. Mehrotra, and T. S. Huang. Supporting similarity queries in mars. Proc. of ACM Conf. on Multimedia, 1997.
[45] M. Ortega, Y. Rui, K. Chakrabarti, A. Warshavsky, S. Mehrotra, and T. S. Huang. Supporting ranked boolean similarity queries in mars. IEEE Transaction on Knowledge and Data Engineering, 10(6):905–925, December 1999.
[46] M. Ortega-Binderberger and S. Mehrotra. Relevance Feedback in Multimedia Databases. in Handbook of Video Databases: Design and Applications, Borko Furht and Oge Marquez eds., 2003.
[47] C. Papageorgiou, M. Oren, and T. Poggio. A general framework for object detection. In Proceedings of the International Conference on Computer Vision, 1998.
[48] K. Porkaew, K. Chakrabarti, and S. Mehrotra. Query refinement for multimedia similarity retrieval in mars. Proceedings of ACM Multimedia, November 1999.
[49] K. Porkaew, S. Mehrota, and M. Ortega. Query reformulation for content based multimedia retrieval in mars. ICMCS, pages 747–751, 1999.
[50] N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. Proceedings of the Eighteenth International Conference on Machine Learning, pages 441–448, August 2001.
[51] Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: A power tool in interactive content-based image retrieval. IEEE Tran on Circuits and Systems for Video Technology, 8(5), Sept 1998.
[52] R. Schapire, Y. Freund, P. Bartlett, and W. Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. In Proceeding of the Fourteenth International Conference on Machine Learning. Morgan Kaufmann, 1997.
[53] G. Schohn and D. Cohn. Less is more: Active learning with support vector machines. In Proceedings of the Seventeenth International Conference on Machine Learning, 2000.
[54] H. Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proceedings of the Fifth Workshop on Computational Learning Theory, pages 287–294. Morgan Kaufmann, 1992.
[55] J. Shawe-Taylor and N. Cristianini. Further results on the margin distribution. In Proceedings of the Twelfth Annual Conference on Computational Learning Theory, pages 278–285, 1999.
[56] J. Smith and S.-F. Chang. Tools and techniques for color image retrieval. Volume 2670 of SPIE Proceedings, pages 1630–1639, 1996.
[57] J. R. Smith and S.-F. Chang. Visualseek: A fully automated content-based image query system. ACM Multimedia Conference, 1996.
[58] S. Tong and E. Chang. Support vector machine active learning for image retrieval. Proc. of ACM Int. Conf. on Multimedia, pages 107–118, October 2001.
[59] S. Tong and D. Koller. Support vector machine active learning with applications to text classification. Proceedings of the 17th International Conference on Machine Learning, pages 401–412, June 2000.
[60] V. Vapnik. Estimation of Dependences Based on Empirical Data. Springer Verlag, 1982.
[61] V. Vapnik. Statistical Learning Theory. Wiley, 1998.
[62] J. Wang, J. Li, and G. Wiederhold. Simplicity: Semantics-sensitive integrated matching for picture libraries. ACM Multimedia Conference, 2000.
[63] J. Z. Wang, G. Wiederhold, O. Firschein, and S. X. Wei. Wavelet-based image indexing techniques with partial sketch retrieval capability. Proceedings of the 4th ADL, May 1997.
[64] H. Wu, H. Lu, and S. Ma. A practical svm-based algorithm for ordinal regression in image retri eval. ACM International Conference on Multimedia, 2003.
[65] L. Wu, C. Faloutsos, K. Sycara, and T. R. Payne. Falcon: Feedback adaptive loop for content-based retrieval. The 26th VLDB Conference, September 2000.
[66] X. S. Zhou and T. S. Huang. Comparing discriminating transformations and svm for learning during multimedia retrieval. Proc. of ACM Conf. on Multimedia, pages 137–146, 2001.
[67] X. S. Zhou and T. S. Huang. Relevance feedback for image retrieval: a comprehensive review. ACM Multimedia Systems Journal, special issue on CBIR, 2003.
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http://www.robotics.stanford.edu/~stong/papers/chang-tong-goh-chang-ieeemm.pdf
