Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2008.2006045
Title: Correlative linear neighborhood propagation for video annotation
Authors: Tang, J. 
Hua, X.-S.
Wang, M.
Gu, Z.
Qi, G.-J.
Wu, X.
Keywords: Graph-based method
Label propagation
Semantic correlation
Video annotation
Issue Date: 2009
Source: Tang, J., Hua, X.-S., Wang, M., Gu, Z., Qi, G.-J., Wu, X. (2009). Correlative linear neighborhood propagation for video annotation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39 (2) : 409-416. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2008.2006045
Abstract: Recently, graph-based semisupervised learning methods have been widely applied in multimedia research area. However, for the application of video semantic annotation in multilabel setting, these methods neglect an important characteristic of video data: The semantic concepts appear correlatively and interact naturally with each other rather than exist in isolation. In this paper, we adapt this semantic correlation into graph-based semisupervised learning and propose a novel method named correlative linear neighborhood propagation to improve annotation performance. Experiments conducted on the Text REtrieval Conference VIDeo retrieval evaluation data set have demonstrated its effectiveness and efficiency. © 2008 IEEE.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/39292
ISSN: 10834419
DOI: 10.1109/TSMCB.2008.2006045
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