title: Correlative linear neighborhood propagation for video annotation.
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.
authors.affiliation: Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China. jhtang@mail.ustc.edu.cn
authors.forename: Jinhui
authors.initials: J
authors.lastname: Tang
authors.forename: Xian-Sheng
authors.initials: XS
authors.lastname: Hua
authors.forename: Meng
authors.initials: M
authors.lastname: Wang
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Gu
authors.forename: Guo-Jun
authors.initials: GJ
authors.lastname: Qi
authors.forename: Xiuqing
authors.initials: X
authors.lastname: Wu