Semi-supervised svm batch mode active learning for image retrieval

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Referenz

S. C. Hoi, R. Jin, J. Zhu, and M. R. Lyu: Semi-supervised svm batch mode active learning for image retrieval. IEEE CVPR, 0:1-7, 2008.

DOI

http://dx.doi.org/10.1109/CVPR.2008.4587350

Abstract

Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to each other. In this paper, we propose a novel scheme that exploits both semi-supervised kernel learning and batch mode active learning for relevance feedback in CBIR. In particular, a kernel function is first learned from a mixture of labeled and unlabeled examples. The kernel will then be used to effectively identify the informative and diverse examples for active learning via a min-max framework. An empirical study with relevance feedback of CBIR showed that the proposed scheme is significantly more effective than other state-of-the-art approaches.

Extended Abstract

Bibtex

@INPROCEEDINGS{4587350,
author={S. C. H. Hoi and Rong Jin and Jianke Zhu and M. R. Lyu},
booktitle={Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
title={Semi-supervised SVM batch mode active learning for image retrieval},
year={2008},
pages={1-7},
keywords={content-based retrieval;feedback;image retrieval;minimax techniques;support vector machines;batch mode active learning;content-based image retrieval;image retrieval;min-max framework;relevance feedback;semisupervised SVM batch mode active learning;semisupervised kernel learning;support vector machine;Application software;Content based retrieval;Image retrieval;Kernel;Learning systems;Machine learning;Sampling methods;State feedback;Support vector machine classification;Support vector machines},
doi={10.1109/CVPR.2008.4587350},
url={  },
ISSN={1063-6919},
month={June},
}

Used References

S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004. http://dx.doi.org/10.1017/CBO9780511804441

K. Brinker. Incorporating diversity in active learning with support vector machines. In Proc. ICML2003, 2003.

O. Chapelle, B. Schölkopf, and A. Zien. Semi-Supervised Learning. The MIT Press, 2006.

D. A. Cohn, Z. Ghahramani, and M. I. Jordan. Active learning with statistical models. In Proc. NIPS, 1995.

C. K. Dagli, S. Rajaram, and T. S. Huang. Leveraging active learning for relevance feedback using an information theoretic diversity measure. In ACM Conferece on Image and Video Retrieval (CIVR), Lecture Notes in Computer Science, pages 123-132, 2006. http://dx.doi.org/10.1007/11788034_13

Y. Guo and D. Schuurmans. Discriminative batch mode active learning. In Proceedings of Advances in Neural Information Processing Systems (NIPS2007), 2007.

S. C. H. Hoi, R. Jin, and M. R. Lyu. Large-scale text categorization by batch mode active learning. In Proc. WWW2006, Edinburgh, England, UK, May 23-26 2006. http://dx.doi.org/10.1145/1135777.1135870

S. C. H. Hoi, R. Jin, J. Zhu, and M. R. Lyu. Batch mode active learning and its application to medical image classification. In Proceedings of the 23rd International Conference on Machine Learning (ICML2006), Pittsburgh, PA, US, June 25-29 2006. http://dx.doi.org/10.1145/1143844.1143897

S. C. H. Hoi and M. R. Lyu. A semi-supervised active learning framework for image retrieval. In Proc. CVPR2005, 2005. http://dx.doi.org/10.1109/CVPR.2005.44

S. C. H. Hoi, M. R. Lyu, and E. Y. Chang. Learning the unified kernel machines for classification. In Proc. KDD 2006, 2006. http://dx.doi.org/10.1145/1150402.1150426

R. Liere and P. Tadepalli. Active learning with committees for text categorization. In Proc. AAAI, 1997.

A. K. McCallum and K. Nigam. Employing EM and pool-based active learning for text classification. In Proc. ICML'98, 1998.

Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: A power tool in interactive content-based image retrieval. IEEE Trans. CSVT, 8(5):644-655, Sept. 1998. http://dx.doi.org/10.1109/76.718510

G. Schohn and D. Cohn. Less is more: Active learning with support vector machines. In Proc. 17th ICML, 2000.

V. Sindhwani, P. Niyogi, and M. Belkin. Beyond the point cloud: from transductive to semi-supervised learning. In Proc. ICML 2005, 2005. http://dx.doi.org/10.1145/1102351.1102455

A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. PAMI, 22(12):1349-1380, 2000. http://dx.doi.org/10.1109/34.895972

S. Tong and E. Chang. Support vector machine active learning for image retrieval. In Proc. ACM Multimedia Conference, 2001. http://dx.doi.org/10.1145/500156.500159

S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In Proc. 17th ICML, 2000.

V. N. Vapnik. Statistical Learning Theory. Wiley, 1998.

L. Wang, K. L. Chan, and Z. Zhang. Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval. In Proc. CVPR, 2003. http://dx.doi.org/10.1109/CVPR.2003.1211412

T. Zhang and R. K. Ando. Analysis of spectral kernel design based semi-supervised learning. In NIPS, 2005.

Links

Full Text

http://www.cse.msu.edu/~rongjin/publications/cvpr-al-08.pdf

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