Content-based image retrieval using optimal feature combination and relevance feedback
文献类型：会议论文
| 作者 | Zhao,Lijun ; Tang,Jiakui |
| 出版日期 | 2010 |
| 会议日期 | 2010-10-22 |
| 关键词 | Computer Applications Content Based Retrieval Experiments Support Vector Machines |
| 页码 | V4436 - V4442 |
| 通讯作者 | Zhao,L. |
| 英文摘要 | With the rapid development of the multimedia technology and Internet, content-based image retrieval (CBIR) has become an active research field at present. Many researches have been done on visual features and their combinations for CBIR, but few on the performance comparison of different visual feature combinations. Therefore, in the paper, different visual feature combinations are firstly compared in retrieval experiments. Moreover, only using low-level features for CBIR cannot achieve a satisfactory measurement performance, since the user's high-level semantics cannot be easily expressed by low-level features. In order to narrow the gap between user query concept and low-level features in CBIR, a multi-round relevance feedback (RF) strategy based on both support vector machine (SVM) and feature similarity is adopted to meet the user's requirement. The experiment results showed that this SVM and feature similarity based relevance feedback using best feature combination can greatly improve the retrieval precision with the number of feedback increasing. |
| 产权排序 | (1) Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, China |
| 会议录 | ICCASM 2010 - 2010 International Conference on Computer Application and System Modeling, Proceedings
 |
| 会议录出版者 | IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States |
| 学科主题 | 摄影测量与遥感 |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| ISBN号 | ISBN-13:9781424472369 |
| 源URL | [http://ir.yic.ac.cn/handle/133337/4744]  |
| 专题 | 烟台海岸带研究所_海岸带信息集成与综合管理实验室 |
| 推荐引用方式 GB/T 7714 | Zhao,Lijun,Tang,Jiakui. Content-based image retrieval using optimal feature combination and relevance feedback[C]. 见:. 2010-10-22. |
入库方式： OAI收割
来源：烟台海岸带研究所
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