Video semantic annotation based on correlative kernel linearneighborhood propagation
基于相关核映射线性近邻传播的视频语义标注

In order to solve the problem that the graph-based semi-supervised learning methods neglect the video consistency in multimedia research area, this paper presented a new method video semantic annotation based on correlative kernel linear neighborhood propagation. The algorithm firstly structured the coefficient by kernel function, and through the coefficient to getting the samples that representative the low feature space. And then according to video correlation modeling, it structured the table between the semantic concepts. Finally, it completed the construction of graph, and used the video information that have been annotated spread to the video that not annotated. After that, the video annotation finished. The experimental results validate the effectiveness and superiority of the proposed method, the process of the video annotation addresses the insufficiency of labeled videos, improves the precision of annotation.