Video-to-Shot Tag Propagation by Graph Sparse Group Lasso

作     者：Zhu, Xiaofeng Huang, Zi Cui, Jiangtao Shen, Heng Tao 

作者机构：Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia Xidian Univ Sch Comp Sci Xian Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON MULTIMEDIA》 (IEEE Trans Multimedia)

年 卷 期：2013年第15卷第3期

页      面：633-646页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Australia Research Council [DP1094678] National Natural Science Foundation of China Australian Research Council [DP1094678] Funding Source: Australian Research Council 

主　　题：Manifold learning sparse coding sparse group lasso structure sparsity video annotation video tagging 

摘      要：Traditional approaches to video tagging are designed to propagate tags at the same level, such as assigning the tags of training videos (or shots) to the test videos (or shots), such as generating tags for the test video when the training videos are associated with the tags at the video-level or assigning tags to the test shot when given a collection of annotated shots. This paper focuses on automatical shot tagging given a collection of videos with the tags at the video-level. In other words, we aim to assign specific tags from the training videos to the test shot. The paper solves the V2S issue by assigning the test shot with the tags deriving from parts of the tags in a part of training videos. To achieve the goal, the paper first proposes a novel Graph Sparse Group Lasso (shorted for GSGL) model to linearly reconstruct the visual feature of the test shot with the visual features of the training videos, i.e., finding the correlation between the test shot and the training videos. The paper then proposes a new tagging propagation rule to assign the video-level tags to the test shot by the learnt correlations. Moreover, to effectively build the reconstruction model, the proposed GSGL simultaneously takes several constraints into account, such as the inter-group sparsity, the intra-group sparsity, the temporal-spatial prior knowledge in the training videos and the local structure of the test shot. Extensive experiments on public video datasets are conducted, which clearly demonstrate the effectiveness of the proposed method for dealing with the video-to-shot tag propagation.