Automatically extracting highlights for TV Baseball programs
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Linking live and replay scenes in broadcasted sports video
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Automatic detection of 'Goal' segments in basketball videos
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A mid-level representation framework for semantic sports video analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Generic play-break event detection for summarization and hierarchical sports video analysis
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Detection of slow-motion replay segments in sports video for highlights generation
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Genre classification system of TV sound signals based on a spectrogram analysis
IEEE Transactions on Consumer Electronics
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
Multi-level semantic analysis for sports video
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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Due to the repetitive and lengthy nature, automatic content-based summarization is essential to extract a more compact and interesting representation of sport video. State-of-the art approaches have confirmed that high-level semantic in sport video can be detected based on the occurrences of specific audio and visual features (also known as cinematic). However, most of them still rely heavily on manual investigation to construct the algorithms for highlight detection. Thus, the primary aim of this paper is to demonstrate how the statistics of cinematic features within play-break sequences can be used to less-subjectively construct highlight classification rules. To verify the effectiveness of our algorithms, we will present some experimental results using six AFL (Australian Football League) matches from different broadcasters. At this stage, we have successfully classified each play-break sequence into: goal, behind, mark, tackle, and non-highlight. These events are chosen since they are commonly used for broadcasted AFL highlights. The proposed algorithms have also been tested successfully with soccer video.