Video Annotation Based on Kernel Linear Neighborhood Propagation
Jinhui Tang, Xian‐Sheng Hua, Guo-Jun Qi, Yan Song, Xiuqing Wu · IEEE Transactions on Multimedia · 2008
The insufficiency of labeled training data for representing the distribution of the entire dataset is a major obstacle in automatic semantic annotation of large-scale video database. Semi-supervised learning algorithms, which attempt to learn from both labeled and unlabeled data, are promising to solve this problem. In this paper, a novel graph-based semi-supervised learning method namedkernellinearneighborhoodpropagation(KLNP) is proposed and applied to video annotation. This approach combines theconsistencyassumption, which is the basic assumption in semi-supervised learning, and thelocallinearembedding(LLE) method in a nonlinear kernel-mapped space. KLNP improves a recently proposed methodlinearneighborhoodpropagation(LNP) by tackling the limitation of its local linear assumption on the distribution of semantics. Experiments conducted on the TRECVID data set demonstrate that this approach outperforms other popular graph-based semi-supervised learning methods for video semantic annotation.