Video semantic analysis based on structure-sensitive anisotropic manifold ranking

As a major family of semi-supervised learning (SSL), graph-based SSL has recently attracted considerable interest in the machine learning community along with application areas such as video semantic analysis. In this paper, we analyze the connections between graph-based SSL and partial differential equation- (PDE) based diffusion. From the viewpoint of PDE-based diffusion, the label propagation in normal graph-based SSL is isotropic accompanied with distance. However, according to the structural assumption, which is one of the two basic assumptions in graph-based SSL, we need to enhance the label propagation between the samples in the same structure while weakening the counterpart between the samples in different structures. Accordingly, we deduce a novel graph-based SSL framework, named structure-sensitive anisotropic manifold ranking (SSAniMR), from PDE-based anisotropic diffusion. Instead of using Euclidean distance only, SSAniMR takes local structural difference into account to make the label propagation anisotropic, which is intrinsically different from the isotropic label propagation process in general graph-based SSL methods. Experiments conducted on the TREC Video Retrieval Evaluation (TRECVID) dataset show that this approach significantly outperforms existing graph-based SSL methods and is effective for video semantic annotation.