Sequence Multi-Labeling: A Unified Video Annotation Scheme With Spatial and Temporal Context

Automatic video annotation is a challenging yet important problem for content-based video indexing and retrieval. In most existing works, annotation is formulated as a multi-labeling problem over individual shots. However, video is by nature informative in spatial and temporal context of semantic concepts. In this paper, we formulate video annotation as a sequence multi-labeling (SML) problem over a shot sequence. Different from many video annotation paradigms working on individual shots, SML aims to predict a multi-label sequence for consecutive shots in a global optimization manner by incorporating spatial and temporal context into a unified learning framework. A novel discriminative method, called sequence multi-label support vector machine (SVM^SML), is accordingly proposed to infer the multi-label sequence for a given shot sequence. In SVM^SML, a joint kernel is employed to model the feature-level and concept-level context relationships (i.e., the dependencies of concepts on the low-level features, spatial and temporal correlations of concepts). A multiple-kernel learning (MKL) algorithm is developed to optimize the kernel weights of the joint kernel as well as the SML score function. To efficiently search the desirable multi-label sequence over the large output space in both training and test phases, we adopt an approximate method to maximize the energy of a binary Markov random field (BMRF). Extensive experiments on TRECVID'05 and TRECVID'07 datasets have shown that our proposed SVM^SML gains superior performance over the state-of-the-art.