Abstract
In this paper, an efficient image matching algorithm for finding the consistent correspondences between two sets of image feature points has been presented. Correct assignments are usually compatible with each other, and thus likely to form a strong cluster. The main idea of the proposed algorithm is to detect this cluster using a local distribution based outlier detection technique. Based on neighbor similarity (or affinity), we first define an inlier score for each assignment in candidate assignment set. Then, we iteratively detect the correct assignments from the candidate assignment set by exploiting the inlier score. Experimental results on several real-world image matching tasks demonstrate the effectiveness and robustness of the proposed algorithm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
