中文摘要：

文章提出了一种高效的图像局部特征匹配算法。在特征描述子构建阶段,提出基于梯度的距离和方向直方图（gradient distance and orientation histogram,GDOH）算法,其特征向量维数仅是SIFT和GLOH描述子的一半,然而却具有与SIFT和GLOH相当的性能;在高维特征空间最近邻搜索阶段,提出基于子向量的索引结构（indexing sub-vectors,ISV）,ISV算法比BBF（Best Bin First）算法具有更高的搜索精度和更快的搜索速度。实验结果证明文中提出的图像局部特征匹配算法（GDOH＋ISV）比目前广泛使用的Lowe的算法[12]（SIFT＋BBF）更加高效。

英文摘要：

Aim.In our opinion,we can devise a local feature matching algorithm that is GDOH（gradient distance and orientation histogram） combined with ISV（indexing sub-vectors） and is more efficient than the widely-used algorithm that is SIFT（scale invariant feature transform） combined with BBF（best bin first） and is proposed by D.G.Lowe in Ref.12.Section 1 of the full paper explains the local feature descriptor that is the core of GDOH.Section 2 presents a procedure that consists of three steps： establishment of ISV structure,feature searching and feature matching.Section 3 presents the experimental results and their analysis.Fig.5 in subsection 3.1 compares the matching performance of GDOH descriptor with that of SIFT descriptor.Table 1 in subsubsection 3.2.1 compares the searching precision and searching time of ISV algorithm respectively with those of BBF algorithm.Fig.6 in subsection 3.3 compares the matching performance of GDOH＋ISV algorithm with that of SIFT＋BBF algorithm.The experimental results and their analysis show preliminarily that our GDOH＋ISV algorithm is indeed more efficient than the SIFT＋BBF algorithm proposed by D.G.Lowe.