Tang Jinhui, Hua Xian-Sheng, Wang Meng, Gu Zhiwei, Qi Guo-Jun, Wu Xiuqing
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China.
IEEE Trans Syst Man Cybern B Cybern. 2009 Apr;39(2):409-16. doi: 10.1109/TSMCB.2008.2006045. Epub 2008 Dec 16.
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.
近年来，基于图的半监督学习方法在多媒体研究领域得到了广泛应用。然而，对于多标签设置下的视频语义标注应用，这些方法忽略了视频数据的一个重要特征：语义概念相互关联且自然交互，而非孤立存在。在本文中，我们将这种语义相关性引入基于图的半监督学习，并提出一种名为相关线性邻域传播的新方法来提高标注性能。在文本检索会议视频检索评估数据集上进行的实验证明了其有效性和高效性。
