An efficient algorithm for local feature matching

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.