K-Dvd-Tree: A High Dimensional Data Index Applying to SIFT Feature Matching

When SIFT algorithm is applied to image feature matching on the mobile platform, it has been found some issues such as slow search rate, high memory consumption and frequency I/O, etc. Aiming at such problems, this paper studies on high dimensional data index structure for SIFT image feature matching, proposed a K-Dvd-tree index structure. This structure divides high dimensional data into multiple subspace using vantage dimension in order to increase the fan-out degree of the tree, controls the number of feature points in leaf node in order to reduce the height of the tree, and ultimately raise the search rate, at the same time, it stores multiple internal nodes at the same level within one page, and makes each leaf node correspond to one page in order to reduce memory consumption, and improve I/O efficiency as well. the experiments show that the performance of this index structure is relatively increased in terms of search rate and I/O efficiency.