Correlative Linear Neighborhood Propagation for Video Annotation

Recently, graph-based semi-supervised learning methods have been widely applied in multimedia research area. However, for the application of video semantic annotation in multi-label setting, these methods neglect an important characteristic of video data: The semantic concepts appear correlatively a...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2009-04, Vol.39 (2), p.409-416
Main Authors: Tang, Jinhui, Hua, Xian-Sheng, Wang, Meng, Gu, Zhiwei, Qi, Guo-Jun, Wu, Xiuqing
Format: Article
Language:English
Subjects:
Annotations
Asia
Conferences
Correlation
Costs
Cybernetics
Graph-based method
Information retrieval
Information science
label propagation
Large-scale systems
Learning
Predictive models
Retrieval
semantic correlation
Semantics
Semisupervised learning
Texts
video annotation
Video compression
Videoconference
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Summary:Recently, graph-based semi-supervised learning methods have been widely applied in multimedia research area. However, for the application of video semantic annotation in multi-label 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 semi-supervised 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.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2008.2006045