Abstract
The ambiguity resulting from repetitive structures in a scene presents a major challenge for image matching. This paper proposes a matching method based on SIFT feature saliency analysis to achieve robust feature matching between images with repetitive structures. The feature saliency within the reference image is estimated by analyzing feature stability and dissimilarity via Monte-Carlo simulation. In the proposed method, feature matching is performed only within the region of interest to reduce the ambiguity caused by repetitive structures. The experimental results demonstrate the efficiency and robustness of the proposed method, especially in the presence of respective structures.
Highlights
Image matching is the core operation in many computer vision tasks [1,2,3,4]
We present a robust image matching strategy based on SIFT saliency
We start to find robust matches for the salient features and perform the triangle constraint to reduce the ambiguities caused by the repetitive features
Summary
Image matching is the core operation in many computer vision tasks [1,2,3,4]. Various approaches focus on improving the appearance distinctiveness and repeatability of the features and design many distinctive descriptors to find more reliable matching pairs [5,6,7,8]. Current research has indicated that existing feature matching methods such as Ratio-Match [7], Self-Match [10], and Mirror-Match criteria [11] are so restrictive that they fail to match repeated features [1]. All these methods make a comparison between the best and second best matches to obtain reliable matches. For the repeated features or similar features from the repetitive structures of a scene, these methods can hardly find unique matches because the best matches are nearly the same as the second matches
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