A Relevance Feedback System for CBIR with Long-term Learning

摘要：

Relevance feedback has been developed to improve retrieval performance effectively in Content Based Image Retrieval (CBIR). This paper introduces a relevance feedback system for CBIR with both short-term relevance feedback and long-term learning. In short-term relevance feedback, query reweighting algorithm, support vector machines (SVM), and genetic algorithm are adopted. In long-term learning, the expanded-judging model with index table is used for analyzing the historical log data. Experimental results show that among short-term feedback algorithms, the SVM gets the best feedback results, and for the use of our proposed expandedjudging model in long-term learning, the recall of the retrieval system is improved more than 30％ in average.

关键词： CBIR Relevance feedback Long-term learning

作者: Lu Hui Huang Xiang-Lin Yang Li-Fang Liu Min

作者单位: Computer School Communication University of China Beijing, China

会议类型: 国际会议

会议名称: 2010年IEEE多媒体信息网络与安全国际会议

会议地点: 南京

会议语种:英文

页码: 700-704

在线出版日期: 2010-11-01（万方平台首次上网日期，不代表论文的发表时间）