Please use this identifier to cite or link to this item:
|Title:||Correlative linear neighborhood propagation for video annotation|
|Authors:||Tang, J. |
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 6, 2017
WEB OF SCIENCETM
checked on Nov 22, 2017
checked on Dec 10, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.