Abstract
Interactively learning from a small sample of unlabeled examples is an enormously challenging task. Relevance feedback and more recently active learning are two standard techniques that have received much attention towards solving this interactive learning problem. How to best utilize the user’s effort for labeling, however, remains unanswered. It has been shown in the past that labeling a diverse set of points is helpful, however, the notion of diversity has either been dependent on the learner used, or computationally expensive. In this paper, we intend to address these issues by proposing a fundamentally motivated, information-theoretic view of diversity and its use in a fast, non-degenerate active learning-based relevance feedback setting. Comparative testing and results are reported and thoughts for future work are presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Rui, Y., Huang, T.S.: Relevance Feedback Techniques in Image Retrieval. In: Lew, M.S. (ed.) Principles of Visual Information Retrieval. Springer, London (2001)
Ko, B.C., Byun, H.: Probabilistic Neural Networks Supporting Multi-Class Relevance Feedback in Region-Based Image Retrieval. In: International Conference on Pattern Recognition (2002)
Dong, A., Bhanu, B.: Active Concept Learning for Image Retrieval in Dynamic Databases. In: International Conference on Computer Vision (2003)
Zhou, X.S., Rui, Y., Huang, T.S.: Exploration of Visual Data. Kluwer Academic Publishers, Dordrecht (2003)
Zhou, X., Huang, T.S.: Small Sample Learning during Multimedia Retrieval using BiasMap. In: IEEE Conference Computer Vision and Pattern Recognition (2001)
Cohn, D.A., Ghahramani, Z., Jordan, M.I.: Active Learning with Statistical Models. In: Tesauro, G., Touretzky, D., Alspector, J. (eds.) Advances in Neural Information Processing Systems, vol. 7. MIT Press, Cambridge (1995)
Roy, N., McCallum, A.: Toward Optimal Active Learning through Monte Carlo Estimation of Error Reduction. In: International Conference on Machine Learning (2001)
Tong, S., Chang, E.: Support Vector Machine Active Learning for Image Retrieval. In: IEEE International Conference on Computer Vision and Pattern Recognition (2001)
Cox, I.J., Minka, T.P., Papathomas, T.V., Yianilos, P.N.: Pichunter: Bayesian Relevance Feedback for Image Retrieval. In: International Conference on Pattern Recognition (1996)
Chen, Y., Wang, J.Z., Krovetz, R.: CLUE: Cluster-based Retrieval of Images by Unsupervised Learning. IEEE Transactions on Image Processing 14(8), 1187–1201 (2005)
Brinker, K.: Incorporating Diversity in Active Learning with Support Vector Machines. In: International Conference on Machine Learning (2003)
Goh, K., Chang, E.Y., Lai, W.-C.: Concept-dependent Multimodal Active Learning for Image Retrieval. In: ACM International Conference on Multimedia (2004)
Shannon, C.E.: A Mathematical Theory of Communication. Bell System Technical Journal 27, 379–423, 623–656 (1948)
Dagli, C.K., Rajaram, S., Huang, T.S.: Combining Diversity-Based Active Learning with Discriminant Analysis in Image Retrieval. In: IEEE International Conference on Information Technology and Applications (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dagli, C.K., Rajaram, S., Huang, T.S. (2006). Leveraging Active Learning for Relevance Feedback Using an Information Theoretic Diversity Measure. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds) Image and Video Retrieval. CIVR 2006. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788034_13
Download citation
DOI: https://doi.org/10.1007/11788034_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36018-6
Online ISBN: 978-3-540-36019-3
eBook Packages: Computer ScienceComputer Science (R0)