Abstract
Manifold learning has become a hot research topic in recent years and is widely used in the area of dimension reduction, information retrieval and ranking, etc. However, how to reconstruct the intrinsic manifold from the observed data points, i.e. what is the proper data point distance measure, is still an open problem. In this paper, we propose to take advantages of the information provided by web-pages and the image-related website link structure to learn the Web image manifold, which better approaches to the intrinsic manifold than those learned by previous methods which use Euclidean alike distances to construct the initial affinity matrix. Experimental results prove the effectiveness of our learned Web image manifold.
This work is done when Xin-Jing Wang is an intern in Microsoft Research Asia.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Belkin, M., Niyogi, P.: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14. MIT Press, Cambridge (2002)
Cai, D., Yu, S.P., Wen, J.R., Ma, W.-Y.: VIPS: a Vision-Based Page Segmentation Algorithm. Microsoft Technical Report, msr-tr-2003-79 (2003)
Chen, Z., Liu, S.P., Liu, W.Y., Pu, G.G., Ma, W.-Y.: Building a Web Thesaurus from Web Link Structure. In: SIGIR (2003)
Fellbaum, C.: WordNet: An Electronical Lexical Database. MIT Press, Cambridge (1998)
He, X.F., Ma, W.Y., Zhang, H.J.: Learning an Image Manifold for Retrieval. ACM Multimedia (2004)
Ma, Y.F., Zhang, H.J.: Contrast-based Image Attention Analysis by Using Fuzzy Growing. ACM Multimedia (2003)
Roweis, S., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)
Silva, V., Tenenbaum, J.B.: Global versus Local Methods in Nonlinear Dimensionality Reduction. Neural Information Processing Systems 15, 705–712 (2003)
Song, R.H., Liu, H.F., Wen, J.R., Ma, W.Y.: Learning Block Importance Models for Web Pages. In: Proceeding of the Thirteenth World Wide Web conference, pp. 203–211 (2004)
Spertus, E.: ParaSite: Mining Structureal Information on the Web. In: Proc. of WWW6, pp. 587–595 (1997)
Tenenbaum, J., Silva, V., Langford, J.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)
Wang, X.J., Ma, W.-Y., Li, X.: Data Driven Approach for Bridgin the Cognitive Gap in Image Retrieval. In: IEEE Conf. on Multimedia and Expo (2004)
Zhou, D.Y., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.: Ranking on Data Manifolds. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16. MIT Press, Cambridge
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, XJ., Ma, WY., Li, X. (2004). Learning Image Manifold Using Web Data. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30542-2_112
Download citation
DOI: https://doi.org/10.1007/978-3-540-30542-2_112
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23977-2
Online ISBN: 978-3-540-30542-2
eBook Packages: Computer ScienceComputer Science (R0)