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SIFT Feature Matching Algorithm with Local Shape Context

Jinqin Zhong, Jieqing Tan, Lichuan Gu

Abstract



Given two or more images of a scene, the ability to match corresponding points between these images is an important component of many computer vision tasks. SIFT (Scale Invariant Feature Transform) is one of the most effective local feature of scale, rotation and illumination invariant, which is widely used in the field of image matching. While there will be a lot mismatches when an image has multiple similar regions. In this paper, an improved SIFT feature matching algorithm with local shape context is put forward. The feature vectors are computed by dominant orientation assignment to each feature point based on elliptical neighboring region and with local shape context, and then the feature vectors are matched by using Euclidean distance and the chi square distance. The experiment indicates that the improved algorithm can reduce mismatch probability and acquire good performance on affine invariance, improves matching results greatly.

Keywords


feature matching, local shape context, elliptical neighbouring region, SIFT algorithm.

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