A Self-Relevance Feedback Method Based on Object Labels

摘要：

Users relevence feedback is often included in many content-based image retrieval (CBIR) systems, and this method is proved to be effective in improving the retrieval result. However, it may cause too much user participation which may make users impatient. To solve this problem, the paper proposes a self-relevance feedback method for CBIR which needs no user involvement. Self-relevance is seldom mentioned in CBIR as it is usually difficult to increase the performance of a system. Based on the concept occurrence vector (COV) used for image retrieval, the proposed method can improve the precision of the retrieval process, which is proved by our experiments. Though the improvement is not very huge, the method make the application of self-relevance feedback in CBIR possible.

关键词： image retrieval self-relevance feedback semantic concept occurrence vector

作者: Jiabin Ruan Yubin Yang

作者单位: State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 215-218

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）