CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Human behaviour consistent relevance feedback model for image retrieval
Jing Liu; Zhiwei Li; Mingjing Li; Hanqing Lu; Songde Ma
2007
Conference NameInternational Conference on Multimedia
Source PublicationProceedings of the 15th International Conference on Multimedia 2007
Conference DateSeptember 24-29, 2007
Conference PlaceAugsburg, Germany
AbstractDue 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.
KeywordGraph Ranking Image Retrieval Relevance Feedback
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13454
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorJing Liu
Recommended Citation
GB/T 7714
Jing Liu,Zhiwei Li,Mingjing Li,et al. Human behaviour consistent relevance feedback model for image retrieval[C],2007.
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