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| Content Provider | Springer Nature Link |
|---|---|
| Author | Meng, Tao Shyu, Mei Ling |
| Copyright Year | 2013 |
| Abstract | The recent development of the digital camera technology and the popularity of social network websites such as Facebook and Youtube have created huge amounts of multimedia data. Multimedia information is ubiquitous and essential in many applications. In order to fill the gap between data and application requirements (or the so-called semantic gap), advanced methods and tools are needed to automatically mine and annotate high-level concepts to assist in associating the low-level features to the high-level concepts directly. It has been shown that concept-concept association can be effective in bridging the semantic gap in multimedia data. In this paper, a concept-concept association information integration and multi-model collaboration framework is proposed to enhance high-level semantic concept detection from multimedia data. Several experiments are conducted and the comparison results demonstrate that the proposed framework outperforms those approaches in the comparison in terms of the Mean Average Precision (MAP) values. |
| Starting Page | 787 |
| Ending Page | 799 |
| Page Count | 13 |
| File Format | |
| ISSN | 13873326 |
| Journal | Information Systems Frontiers |
| Volume Number | 16 |
| Issue Number | 5 |
| e-ISSN | 15729419 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2013-04-06 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Multi-model collaboration Semantic gap Association information integration Concept detection Business Information Systems Management of Computing and Information Systems Systems Theory, Control Operations Research/Decision Theory |
| Content Type | Text |
| Resource Type | Article |
| Subject | Theoretical Computer Science Computer Networks and Communications Information Systems Software |
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