Indexing Sub-Vector Distance for High-Dimensional Feature Matching

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.