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| Content Provider | Springer Nature Link |
|---|---|
| Author | Zhu, Songhao Liu, Yuncai |
| Copyright Year | 2008 |
| Abstract | Grouping video content into semantic segments and classifying semantic scenes into different types are the crucial processes to content-based video organization, management and retrieval. In this paper, a novel approach to automatically segment scenes and semantically represent scenes is proposed. Firstly, video shots are detected using a rough-to-fine algorithm. Secondly, key-frames within each shot are selected adaptively with hybrid features, and redundant key-frames are removed by template matching. Thirdly, spatio-temporal coherent shots are clustered into the same scene based on the temporal constraint of video content and visual similarity between shot activities. Finally, under the full analysis of typical characters on continuously recorded videos, scene content is semantically represented to satisfy human demand on video retrieval. The proposed algorithm has been performed on various genres of films and TV program. Promising experimental results show that the proposed method makes sense to efficient retrieval of interesting video content. |
| Starting Page | 183 |
| Ending Page | 205 |
| Page Count | 23 |
| File Format | |
| ISSN | 13807501 |
| Journal | Multimedia Tools and Applications |
| Volume Number | 42 |
| Issue Number | 2 |
| e-ISSN | 15737721 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2008-10-08 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Video content analysis Shot segmentation Scene clustering Semantic representation Special Purpose and Application-Based Systems Data Structures, Cryptology and Information Theory Computer Communication Networks Multimedia Information Systems |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications Hardware and Architecture Software Media Technology |
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