Abstract: Feature matching for wide baseline images is an extremely challenging task in computer vision applications.A large number of outliers are inevitably included in the initial matching results due to significant changes between views of wide baseline images.An inlier selection algorithm called K nearest neighbor consistency (KNNC) was proposed to efficiently select matches with high reliability from initial feature matching results of wide baseline images.An affine-invariant structure similarity is utilized to measure the degree of structure similarity between two groups of K nearest neighboring features.Adopting the coarse-to-fine strategy,KNNC algorithm selects inliers by the processes of K nearest neighbor correspondence consistency checking and K nearest neighbor structure consistency checking.Experimental results show that the proposed algorithm approximates or surpasses several state-of-the-art inlier selection algorithms in performance on precision,recall and computational time,and is applicable to wide baseline images with large differences in viewpoint,scale and rotation.

Key words: Wide baseline image,Feature matching,Inlier selection,K nearest neighbor,Structure similarity