An Efficient Method for Automatic Video Annotation and Retrieval in Visual Sensor Networks
Automatic video annotation has become an important issue in visual sensor networks, due to the existence of a semantic gap. Although it has been studied extensively, semantic representation of visual information is not well understood. To address the problem of pattern classification in video annota...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/832512 |
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| _version_ | 1832553125717737472 |
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| author | Jiangfan Feng Wenwen Zhou |
| author_facet | Jiangfan Feng Wenwen Zhou |
| author_sort | Jiangfan Feng |
| collection | DOAJ |
| description | Automatic video annotation has become an important issue in visual sensor networks, due to the existence of a semantic gap. Although it has been studied extensively, semantic representation of visual information is not well understood. To address the problem of pattern classification in video annotation, this paper proposes a discriminative constraint to find a solution to approach the sparse representative coefficients with discrimination. We study a general method of discriminative dictionary learning which is independent of the specific dictionary and classifier learning algorithms. Furthermore, a tightly coupled discriminative sparse coding model is introduced. Ultimately, the experimental results show that the provided method offers a better video annotation method that cannot be achieved with existing schemes. |
| format | Article |
| id | doaj-art-62db7d3f127043d98013e7fd0559f241 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-62db7d3f127043d98013e7fd0559f2412025-02-03T05:55:22ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-03-011010.1155/2014/832512832512An Efficient Method for Automatic Video Annotation and Retrieval in Visual Sensor NetworksJiangfan FengWenwen ZhouAutomatic video annotation has become an important issue in visual sensor networks, due to the existence of a semantic gap. Although it has been studied extensively, semantic representation of visual information is not well understood. To address the problem of pattern classification in video annotation, this paper proposes a discriminative constraint to find a solution to approach the sparse representative coefficients with discrimination. We study a general method of discriminative dictionary learning which is independent of the specific dictionary and classifier learning algorithms. Furthermore, a tightly coupled discriminative sparse coding model is introduced. Ultimately, the experimental results show that the provided method offers a better video annotation method that cannot be achieved with existing schemes.https://doi.org/10.1155/2014/832512 |
| spellingShingle | Jiangfan Feng Wenwen Zhou An Efficient Method for Automatic Video Annotation and Retrieval in Visual Sensor Networks International Journal of Distributed Sensor Networks |
| title | An Efficient Method for Automatic Video Annotation and Retrieval in Visual Sensor Networks |
| title_full | An Efficient Method for Automatic Video Annotation and Retrieval in Visual Sensor Networks |
| title_fullStr | An Efficient Method for Automatic Video Annotation and Retrieval in Visual Sensor Networks |
| title_full_unstemmed | An Efficient Method for Automatic Video Annotation and Retrieval in Visual Sensor Networks |
| title_short | An Efficient Method for Automatic Video Annotation and Retrieval in Visual Sensor Networks |
| title_sort | efficient method for automatic video annotation and retrieval in visual sensor networks |
| url | https://doi.org/10.1155/2014/832512 |
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