Name
Abstract | ||
|---|---|---|
This paper proposes a mining-based method to achieve event detection for broadcasting tennis videos. Utilizing visual and aural information, we extract some high-level features to describe video segments. The audiovisual features are further transformed to symbolic streams and an efficient mining technique is applied to derive all frequent patterns that characterize tennis events. After mining, we categorize frequent patterns into several kinds of events and therefore achieve event detection for tennis videos by checking the correspondence between mined patterns and events. The experimental results show that the proposed approach is a promising way to detect events in broadcasting tennis video. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICME.2008.4607725 | 2008 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-4 |
Keywords | Field | DocType |
Event detection, data mining, tennis videos | Categorization,Computer vision,Broadcasting,Pattern recognition,Computer science,Feature extraction,Speech recognition,Artificial intelligence,Audio signal processing,Quantization (signal processing) | Conference |
Citations | PageRank | References |
16 | 0.92 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-chun Tien | 1 | 133 | 7.65 |
| Yi-tang Wang | 2 | 16 | 1.25 |
| Chen-Wei Chou | 3 | 22 | 1.47 |
| Kuei-Yi Hsieh | 4 | 30 | 1.90 |
| Wei-ta Chu | 5 | 611 | 56.68 |
| Ja-ling Wu | 6 | 1569 | 168.11 |