Semi-supervised Concept Detection by Learning the Structure of Similarity Graphs
We present an approach for detecting concepts in images by a graph-based semi-supervised learning scheme. The proposed approach builds a similarity graph between both the labeled and unlabeled images of the collection and uses the Laplacian Eigemaps of the graph as features for training concept detectors. Therefore, it offers multiple options for fusing different image features. In addition, we present an incremental learning scheme that, given a set of new unlabeled images, efficiently performs the computation of the Laplacian Eigenmaps. We evaluate the performance of our approach both on synthetic datasets and on MIR Flickr, comparing it with high-performance state-of-the-art learning schemes with competitive and in some cases superior results.
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- 1.Wang, M., Hua, X.-S., Tang, J., Hong, R.: Beyond distance measurement: constructing neighborhood similarity for video annotation. TMM 11(3), 465–476 (2009)Google Scholar
- 2.Zhu, X.: Semi-supervised learning with graphs. PhD Thesis, Carnegie Mellon University (2005) 0-542-19059-1Google Scholar
- 3.Zhou, D., Bousquet, O., Navin Lal, T., Weston, J., Schölkopf, B.: Learning with Local and Global Consistency. In: Advances in NIPS, vol. 16, pp. 321–328. MIT Press (2004)Google Scholar
- 4.Tang, J., et al.: Inferring semantic concepts from community contributed images and noisy tags. ACM Multimedia, 223–232 (2009)Google Scholar
- 5.Chen, X., et al.: Efficient large scale image annotation by probabilistic collaborative multi-label propagation. ACM Multimedia, 35–44 (2010)Google Scholar
- 7.Macskassy, S.A., Provost, F.: Classification in Networked Data: A Toolkit and a Univariate Case Study. Journal of Machine Learning Research 8, 935–983 (2007)Google Scholar
- 9.Jia, P., Yin, J., Huang, X., Hu, D.: Incremental Laplacian eigenmaps by preserving adjacent information between data points. PR Letters 30(16), 1457–1463 (2009)Google Scholar
- 10.Leyffer, S., Mahajan, A.: Nonlinear Constrained Optimization: Methods and Software. Preprint ANL/MCS-P1729-0310 (2010)Google Scholar
- 13.Huiskes, M.J., Michael, S., Lew, M.S.: The MIR Flickr Retrieval Evaluation. In: Proceedings of ACM Intern. Conference on Multimedia Information Retrieval (2008)Google Scholar
- 14.Hare, J.S., Lewis, P.H.: Automatically annotating the MIR Flickr dataset. In: ACM ICMR, pp. 547–556 (2010)Google Scholar
- 15.Guillaumin, M., Verbeek, J., Schmid, C.: Multimodal semi supervised learning for image classification. In: Proceedings of IEEE CVPR Conference, pp. 902–909 (2010)Google Scholar