Name
Abstract | ||
|---|---|---|
Soccer highlight detection is an active research topic in recent years. One of the difficult problems is how to effectively fuse multi-modality cues, i.e. audio, visual and textual information, to improve the detection performance. This paper proposes a novel two-dependence Bayesian network (2d-BN) based fusion approach to soccer highlight detection. 2d-BN is a particular Bayesian network which assumes that each variable depends on two other variables at most. Through this assumption, 2d-BN can not only characterize the relationships among features but also be trained efficiently. Extensive experiments demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICME.2006.262858 | 2006 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO - ICME 2006, VOLS 1-5, PROCEEDINGS |
Keywords | Field | DocType |
production,feature extraction,sun,sport,support vector machines,fuses,machine learning,bayesian network,bayesian methods,robustness | Data mining,Textual information,Computer science,Robustness (computer science),Artificial intelligence,Fuse (electrical),Pattern recognition,Support vector machine,Feature extraction,Bayesian network,Support vector machine classification,Machine learning,Bayesian probability | Conference |
Citations | PageRank | References |
9 | 0.71 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianguo Li | 1 | 377 | 35.38 |
| Tao Wang | 2 | 238 | 23.70 |
| Wei Hu | 3 | 182 | 14.17 |
| Mingliang Sun | 4 | 9 | 1.05 |
| Yimin Zhang | 5 | 359 | 28.66 |