Feature-based Similarity Retrieval in Content-based Image Retrieval

摘要：

Content-based image retrieval (CBIR), providing query by image examples other than key words, is a hot topic in recent years. Querying by words mainly depends on the performance of crawler, whereas query by example is more unpredictable, since feature extraction is still challenging due to the rich content of the image. This paper focuses on the issue of similarity retrieval in high-dimensional space, a problem of performing nearest-neighbor queries efficiently and effectively over large highdimensional databases. Although some arguments have advocated that nearest-neighbor queries do not even make sense for high-dimensional data, we review the existing techniques of working in vector space of high dimension, and provide our unique view towards the issue of time complexity and precision during similarity retrieval in CBIR.

关键词： similarity retrieval approximate nearest neighbor search high dimension mutual information

作者: JunlingXu Baowen Xu Shuaiqiu Men

作者单位: School of Computer Science and Engineering, Southeast University, China Shanghai Key Laboratory of C Department of Computer Science and Technology, Nanjing University, Nanjing, China School of Engineering, Hong Kong University of Science and Technology, Hong Kong, China

会议类型: 国际会议

会议名称: 2010 Seventh Web Information System and Applications Conference(第七届全国web信息系统及其应用学术会议)

会议地点: 呼和浩特

会议语种:英文

页码: 215-219

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）