Automatic Genre Classification of Sports News Video Using Features of Playfield and Motion Vector 


Vol. 14,  No. 2, pp. 89-98, Apr.  2007
10.3745/KIPSTB.2007.14.2.89


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  Abstract

For browsing, searching, and manipulating video documents, an indexing technique to describe the video contents is required. Until now, the indexing process is mostly carried out by specialists who manually assign a few keywords to the video contents and thereby this work becomes an expensive and time consuming task. Therefore, automatic classification of video content is necessary. We propose a fully automatic and computationally efficient method for analysis and summarization of sports news videos for 5 sports news video such as soccer, golf, baseball, basketball and volleyball. First of all, sports news videos are classified as anchor-person shots, and the other shots are classified as news reports shots. Shot classification is based on image preprocessing and color features of the anchor-person shots. We then use the dominant color of the field and motion features for analysis of sports shots. Finally, sports shots are classified into five genre type. We achieved an overall average classification accuracy of 75% on sports news videos with 241 scenes. Therefore, the proposed method can be further used to search news video for individual sports news and sports highlights.

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[IEEE Style]

M. Y. Song, S. H. Jang, H. J. Cho, "Automatic Genre Classification of Sports News Video Using Features of Playfield and Motion Vector," The KIPS Transactions:PartB , vol. 14, no. 2, pp. 89-98, 2007. DOI: 10.3745/KIPSTB.2007.14.2.89.

[ACM Style]

Mi Young Song, Sang Hyun Jang, and Hyung Je Cho. 2007. Automatic Genre Classification of Sports News Video Using Features of Playfield and Motion Vector. The KIPS Transactions:PartB , 14, 2, (2007), 89-98. DOI: 10.3745/KIPSTB.2007.14.2.89.

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