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Video Event Mining via Multimodal Content Analysis and Classification
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chen, Min Chen, Shu-Ching Shyu, Mei-Ling Zhang, Chengcui |
| Copyright Year | 2007 |
| Abstract | As digital video data becomes more and more pervasive , the issue of mining information from video data becomes increasingly important. In this paper, we present an effective multimedia data mining framework for event mining with its application in the automatic extraction of goal events in soccer videos. The extracted goal events can be used for high-level indexing and selective browsing of soccer videos. The proposed multimedia data mining framework first analyzes the soccer videos by using multimodal features (visual and audio features). Then the data pre-filtering step is performed on raw video features with the aid of domain knowledge, and the cleaned data are used as the input data in the data mining process using the Nearest Neighbor with Generalization (NNG) scheme, a generalized Instance-Based Learning (IBL) mechanism. The proposed framework fully exploits the rich semantic information contained in visual and audio features for soccer video data, and incorporates a data mining process for effective detection of soccer goal events. This framework has been tested using soccer videos with different styles as produced by different broadcasters. The results are promising and can provide a good basis for analyzing the high-level structure of video content. |
| Starting Page | 234 |
| Ending Page | 258 |
| Page Count | 25 |
| File Format | PDF HTM / HTML |
| DOI | 10.1007/978-1-84628-799-2_12 |
| Alternate Webpage(s) | https://info.cs.uab.edu/zhang/Promotion-Full-Professor-Application/publication-PDFs/bookchapter-video-event-mining-min.pdf |
| Alternate Webpage(s) | https://info.cs.uab.edu/zhang/CV.pdf |
| Alternate Webpage(s) | https://cis.uab.edu/zhang/CV.pdf |
| Alternate Webpage(s) | https://doi.org/10.1007/978-1-84628-799-2_12 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
