Keypoint Recognition with Two-Stage Randomized Trees

Abstract

SUMMARYThis paper proposes a high-precision, high-speed keypoint matching method using two-stage randomized trees (RTs). The keypoint classification uses conventional RTs for high-precision, real-time keypoint matching. However, the wide variety of view transformations for templates expressed by RTs make it diffidult to achieve high-precision classification for all transformations with a single RTs. To solve this problem, the proposed method classifies the template view transformations in the first stage and then, in the second stage, classifies the keypoints using the RTs that corresponds to each of the view transformations classified in the first stage. Testing demonstrated that the proposed method is 88.5% more precise than SIFT, and 63.5% more precise than using conventional RTs for images in which the viewpoint of the object is rotated by 70 degrees. We have also shown that the proposed method supports real-time keypoint matching at 12 fps.

An Improved Randomized Local Binary Features for Keypoints Recognition

“…The basic sampling operators are also two size-fixed small grids pre-selected from keypoint neighborhood. S. Shimizu and H. Fujiyoshi [ 17 ] proposed using two-stage randomized trees for keypoints recognition. The viewpoints of the input image are classified in the first stage; in the second stage, keypoint classification is performed using the RTs trained with image viewpoints that are near those classified in the first stage.…”

“…The other way to solve this problem is to treat keypoints matching as a classification problem, in which each class corresponds to the set of all possible views of such a point. RandomTrees classifier [ 15 , 16 ] and its variant [ 17 ], RandomFerns classifier [ 15 , 18 ] and restricted Boltzmann machine [ 19 ] are proposed to recognize keypoints. However, these classifier-based methods focus their attention on classifier improvement but ignore improving the quality of binary feature space.…”