An Updating Scheme based on Long-Term Relevance Feedback Learning in VAST system

摘要：

In our earlier works on VAST(VisuAl & SemanTic image search)system,the semantic network effectively associated keywords and visual feature clusters 1.However,we only concerned about the construction of the semantic network before,and did not consider the updating of the semantic network.In this paper,an updating scheme base on Long-Term Relevance Feedback Learning is proposed to update the semantic network in VAST system.The updating scheme keeps up the characteristic of automatic retrieval for the semantic network,and further makes full use of the user’s feedback information.Therefore,the semantic network with the updating scheme gives a good tradeoff between utilizing the user’s feedback and avoiding the “lazy user problem.The experimental results show the effectiveness of the proposed updating scheme.

作者: Ruhan He Zhiguang Zhang

作者单位: College of Computer Science,Wuhan University of Science and Engineering,Wuhan,430073,China College of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan 43007

会议类型: 国际会议

会议名称: 第一届智能网络与智能系统国际会议(ICINIS 2008)(The First International Conference on Intelligent Networks and Intelligent Systems)

会议地点: 武汉

会议语种:英文

在线出版日期: 2008-11-01（万方平台首次上网日期，不代表论文的发表时间）