Re-ranking algorithm using clustering and relevance feedback for image retrieval

摘要：

In conventional content-based image retrieval (CBIR) systems, it is often observed that images visually dissimilar to a query image are ranked high in retrieval results, which affects the retrieval effectiveness. To remedy this problem, we re-rank the retrieved images via clustering and relevance feedback. Based on conventional CBIR system, the retrieved images are analyzed using clustering method, and the weights of each feature component are updated. Then, the rank of the results is adjusted according to the distance of a cluster from a query. Experimental results show that our re-ranking algorithm achieves a more rational ranking of retrieval results compared with existing methods.

关键词： image retrieval clustering algorithm similarity metric relevance feedback

作者: ZHANG Xu-bo PENG Jin-ye

作者单位: School of Information Science and Technology Northwest University Xian, China

会议类型: 国际会议

会议名称: 2010 International Conference on Educational and Network Technology(2010教育与网络技术国际会议 ICENT 2010)

会议地点: 秦皇岛

会议语种:英文

页码: 237-239

在线出版日期: 2010-06-25（万方平台首次上网日期，不代表论文的发表时间）