Human behaviour consistent relevance feedback model for image retrieval
文献类型：会议论文
| 作者 | Jing Liu ; Zhiwei Li; Mingjing Li; Hanqing Lu; Songde Ma
|
| 出版日期 | 2007 |
| 会议日期 | September 24-29, 2007 |
| 会议地点 | Augsburg, Germany |
| 关键词 | Graph Ranking Image Retrieval Relevance Feedback |
| 英文摘要 | Due to the well known semantic gap, content based image retrieval is a difficult problem. To bridge it, relevance feedback as an effective solution has been extensively studied in literatures. However, existing methods follow a single-line searching philosophy, which may lead to a local optimum in search space. To address the problem, we propose a human behavior consistent relevance feedback model for image retrieval in this paper. Simulating human behaviors, the proposed model enable the user to perform relevance feedback in three manners: Follow up, Go back, and Restart. Each manner is a way for the user to provide the system with his or her opinions about search results. The accumulated feedback information can be used to refine the user query and regulate the similarity metric. We adopt the graph ranking algorithm to model the retrieval process. Experiments conducted on standard Corel dataset and Pascal VOC 2006 dataset demonstrate the effectiveness of the proposed mechanism. |
| 会议录 | Proceedings of the 15th International Conference on Multimedia 2007
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13454]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 通讯作者 | Jing Liu |
| 推荐引用方式 GB/T 7714 | Jing Liu,Zhiwei Li,Mingjing Li,et al. Human behaviour consistent relevance feedback model for image retrieval[C]. 见:. Augsburg, Germany. September 24-29, 2007. |
入库方式： OAI收割
来源：自动化研究所
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