Name
Abstract | ||
|---|---|---|
Retrieving videos by key words requires semantic knowledge of the videos. However, manual video annotation is very costly and time consuming. Most works reported in literatures focus on annotating a video shot with either only one semantic concept or a fixed number of words. In this paper, we propose a new approach to automatically annotate a video shot with a non-fixed number of semantic concepts and to retrieve videos based on text queries. First, a simple but efficient method is presented to automatically extract Semantic Candidate Set (SCS) for a video shot based on visual features. Then, the final annotation set is obtained from SCS by Bayesian Inference. Finally, a new way is proposed to rank the retrieved key frames according to the probabilities obtained during Bayesian Inference. Experiments show that our method is useful in automatically annotating video shots and retrieving videos by key words. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-69423-6_28 | MMM |
Keywords | Field | DocType |
automatic video annotation,bayesian inference,manual video annotation,key word,retrieving video,efficient method,semantic knowledge,annotating video shot,key frame,video shot,semantic concept | Semantic memory,Computer vision,Annotation,Bayesian inference,Information retrieval,Computer science,Video annotation,Image retrieval,Bayesian network,Artificial intelligence,Key frame,Semantics | Conference |
Volume | ISSN | ISBN |
4351 | 0302-9743 | 3-540-69421-8 |
Citations | PageRank | References |
1 | 0.37 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fangshi Wang | 1 | 21 | 4.74 |
| De Xu | 2 | 158 | 13.08 |
| Wei Lu | 3 | 66 | 17.41 |
| Weixin Wu | 4 | 1 | 0.37 |