Indexing sub-vector distance for high-dimensional feature matching

Heng Yang, Qing Wang, Zhoucan He

科研成果: 会议稿件论文同行评审

1 引用 (Scopus)

摘要

High-dimensional feature matching based on nearest neighbors search is a core part of many image-matching based problems in computer vision which are solved by local invariant features. In this paper, we propose a new indexing structure for the high-dimensional feature matching, which is based on the distance of the sub-vectors. In addition, we employ an effective image-similarity measure of two images based on the exponential distribution of the Euclidean distance between matched feature vectors. Experimental results have demonstrated the efficiency and effectiveness of the proposed methods in extensive image matching and image retrieval applications.

源语言英语
DOI
出版状态已出版 - 2008
活动2008 19th British Machine Vision Conference, BMVC 2008 - Leeds, 英国
期限: 1 9月 20084 9月 2008

会议

会议2008 19th British Machine Vision Conference, BMVC 2008
国家/地区英国
Leeds
时期1/09/084/09/08

指纹

探究 'Indexing sub-vector distance for high-dimensional feature matching' 的科研主题。它们共同构成独一无二的指纹。

引用此