Instance detection by keypoint matching beyond the nearest neighbor. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Uzyıldırım, Furkan; Özuysal, Mustafa
• Is Part Of: Signal, image and video processing. Volume 10:Issue 8(2016)
• Page Start: 1527
• Page End: 1534
• Publication Date: 2016-11
• Subjects: Computer vision -- Keypoint matching -- Object detection; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0966-6
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract The binary descriptors are the representation of choice for real-time keypoint matching. However, they suffer from reduced matching rates due to their discrete nature. We propose an approach that can augment their performance by searching in the top K near neighbor matches instead of just the single nearest neighbor one. To pick the correct match out of the K near neighbors, we exploit statistics of descriptor variations collected for each keypoint in an off-line training phase. This is a similar approach to those that learn a patch specific keypoint representation. Unlike these approaches, we only use a keypoint specific score to rank the list of K near neighbors. Since this list can be efficiently computed with approximate nearest neighbor algorithms, our approach scales well to large descriptor sets.