Paper
24 June 2005 Unsupervised sports video scene clustering and its applications to story units detection
Weigang Zhang, Qixiang Ye, Liyuan Xing, Qingming Huang, Wen Gao
Author Affiliations +
Proceedings Volume 5960, Visual Communications and Image Processing 2005; 59601C (2005) https://doi.org/10.1117/12.631389
Event: Visual Communications and Image Processing 2005, 2005, Beijing, China
Abstract
In this paper, we present a new and efficient clustering approach for scene analysis in sports video. This method is generic and does not require any prior domain knowledge. It performs in an unsupervised manner and relies on the scene likeness analysis of the shots in the video. The two most similar shots are merged into the same scene in each iteration. And this procedure is repeated until the merging stop criterion is satisfied. The stop criterion is defined based on a J value which is defined according to the Fisher Discriminant Function. We call this method J-based Scene Clustering. By using this method, the low-level video content representation-shots could be clustered into the midlevel video content representation-scenes, which are useful for high-level sports video content analysis such as playbreak parsing, story units detection, highlights extraction and summarization, etc. Experimental results obtained from various types of broadcast sports videos demonstrate the efficacy of the proposed approach. Moreover, in this paper, we also present a simple application of our scene clustering method to story units detection in periodic sports videos like archery video, diving video and so on. The experimental results are encouraging.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Weigang Zhang, Qixiang Ye, Liyuan Xing, Qingming Huang, and Wen Gao "Unsupervised sports video scene clustering and its applications to story units detection", Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59601C (24 June 2005); https://doi.org/10.1117/12.631389
Lens.org Logo
CITATIONS
Cited by 15 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Video

RGB color model

Visualization

Video processing

Analytical research

Cameras

Semantic video

Back to Top