Abstract
The insufficiency of labeled training samples for representing the distribution of the entire data set (include labeled and unlabeled) is a major obstacle in automatic semantic annotation of large-scale video database. Semi-supervised learning algorithms, which attempt to learn from both labeled and unlabeled data, are promising to solve this problem. In this paper, we present a novel semi-supervised approach named Kernel based Local Neighborhood Propagation (Kernel LNP) for video annotation. This approach combines the consistency assumption and the Local Linear Embedding (LLE) method in a nonlinear kernel-mapped space, which improves a recently proposed method Local Neighborhood Propagation (LNP) by tackling the limitation of its local linear assumption on the distribution of semantics. Experiments conducted on the TRECVID data set demonstrate that this approach can obtain a more accurate result than LNP for video semantic annotation.
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References
Chapelle, O., Zien, A., Scholkopf, B.: Semi-supervised Learning. MIT Press, Cambridge (2006)
Yan, R., Naphade, M.: Semi-supervised Cross Feature Learning for Semantic Concept Detection in Videos. In: IEEE Conference on Computer Vision and Pattern Recognition, San Diego, July 2005, IEEE, Los Alamitos (2005)
Wang, M., et al.: Automatic Video Annotation by Semi-supervised Learning with Kernel Density Estimation. In: ACM Multimedia, Santa Barbara, October 2006, ACM Press, New York (2006)
Grira, N., Crucianu, M., Boujemaa, N.: Semi-Supervised Image Database Categorization using Pairwise Constrains. In: IEEE International Conference on Image Processing, Genova, September 2005, IEEE Computer Society Press, Los Alamitos (2005)
Zhou, D., et al.: Learning with Local and Global Consistency. In: Advances in Neural Information Processing System (2003)
Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)
Belkin, M., Niyogi, P.: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. In: Advances in Neural Information Processing System (2000)
Wang, F., et al.: Semi-Supervised Classification Using Linear Neighborhood Propagation. In: IEEE Conference on Computer Vision and pattern Recognition, New York City, June 2006, IEEE, Los Alamitos (2006)
Wang, F., Zhang, C.: Label Propagation through Linear Neighborhoods. In: 23rd International Conference on Machine Learning, Pittsburgh (June 2006)
Saul, L.K., Roweis, S.T.: Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds. Journal of Machine Learning Research, 119–155 (2003)
Belkin, M., Matveeva, I., Niyogi, P.: Regularization and Semisupervised Learning on Large Graphs. In: Conference on Learning Theory (2004)
Perona, P., Malik, J.: Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Transaction on Pattern Analysis and Machine Intelligence 12(7) (1990)
Guidelines for the TRECVID 2005 Evaluation. http://www-nlpir.nist.gov/projects/tv2005/tv2005.html
Over, P., et al.: TRECVID 2005 - An Overview. In: TREC Video Retrieval Evaluation Online Proceedings, NIST (2005)
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Tang, J., Hua, XS., Song, Y., Qi, GJ., Wu, X. (2007). Kernel-Based Linear Neighborhood Propagation for Semantic Video Annotation. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_87
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DOI: https://doi.org/10.1007/978-3-540-71701-0_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
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