A Hierarchical Semantics-Matching Approach for Sports Video Annotation

  • Authors:

  • Chao Liang;Yi Zhang;Changsheng Xu;Jinqiao Wang;Hanqing Lu

  • Affiliations:

  • National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190;National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190;National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190;National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190;National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190

  • Venue:
  • PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Text facilitated sports video analysis has achieved extensive success in video indexing, retrieval and summarization. A commonly adopted basis in previous work is the separate alignment of timestamps between sports video and game text, which isn't a robust method for generic cross-media analysis. In this paper, we propose a hierarchical semantics-matching approach to annotate sports video. Our key idea is to link video and text with high-level semantics rather than low-level features and find the optimal video-text alignment based on the integral structure rather than individual conditions. For accurate event location, the whole algorithm is implemented in a hierarchical way to generate both refined and accurate video annotation result. Experiments conducted on both basketball and football matches demonstrate that our proposed approach is effective for text facilitated sports video annotation.