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| Content Provider | Springer Nature Link |
|---|---|
| Author | Xiao, Zhongmiao Qi, Xiaojun |
| Copyright Year | 2013 |
| Abstract | We propose a complementary relevance feedback-based content-based image retrieval (CBIR) system. This system exploits the synergism between short-term and long-term learning techniques to improve the retrieval performance. Specifically, we construct an adaptive semantic repository in long-term learning to store retrieval patterns of historical query sessions. We then extract high-level semantic features from the semantic repository and seamlessly integrate low-level visual features and high-level semantic features in short-term learning to effectively represent the query in a single retrieval session. The high-level semantic features are dynamically updated based on users’ query concept and therefore represent the image’s semantic concept more accurately. Our extensive experimental results demonstrate that the proposed system outperforms its seven state-of-the-art peer systems in terms of retrieval precision and storage space on a large scale imagery database. |
| Starting Page | 2157 |
| Ending Page | 2177 |
| Page Count | 21 |
| File Format | |
| ISSN | 13807501 |
| Journal | Multimedia Tools and Applications |
| Volume Number | 73 |
| Issue Number | 3 |
| e-ISSN | 15737721 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2013-09-12 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Content-based image retrieval Relevance feedback model Semantic features Long-term learning Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications Hardware and Architecture Software Media Technology |
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