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

Abstract--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 (SVMSML), is accordingly proposed to infer the multi-label sequence for a given shot sequence. In SVMSML, a joint kernel is employed to model the feature-level and concept-level context relationships (i.e., the dependencies of concepts ...

• Type: Journal
• Year: 2010
• Where: TMM
• Authors: Yuanning Li, YongHong Tian, Ling-Yu Duan, Jingjing Yang, Tiejun Huang, Wen Gao
• Keywords: Management, Multi-label Sequence, Shot Sequence, TMM 2010, Video Annotation