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Di Zhong, Shih-Fu Chang. Structure Parsing and Event Detection for Sports Video. ADVENT Technical Report #091 Columbia University, December 2000.

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For video indexing, typically videos are first processed to obtain constituent objects and features. These extracted entities provide an intermediate content model to effectively describe videos. In earlier chapters, we have studied temporal segmentation that generates elementary video shots, and spatial-temporal segmentation that extracts video regions and objects. We also developed feature matching algorithms and query methods to support visual similarity search. These works provide very useful tools for accessing online digital videos. However, the objects and features we have extracted so far contain little information at the semantic level. To solve this problem, we can explore the knowledge and constraints in specific domain and apply domain-specific rules and/or unsupervised machine learning techniques. In this chapter, we present an event detection and structure parsing system for sports videos. This system is built on top of segmentation and search techniques we have presented in prior chapters. We also demonstrate a summarization and browsing interface that allows users to easily access content structure and event index of video data


Di Zhong
Shih-Fu Chang

BibTex Reference

   Author = {Zhong, Di and Chang, Shih-Fu},
   Title = {Structure Parsing and Event Detection for Sports Video},
   Institution = {Columbia University},
   Month = {December},
   Year = {2000}

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