Filtering out nondiscriminative keypoints by geometry based keypoint constellations

Keypoint-based object detection typically utilizes the nearest neighbour matching technique in order to mach discriminative and reject nondiscriminative keypoints. A detected keypoint is found to be nondiscriminative if it is similar enough to more than one model keypoint. This strategy does not always prove efficient, especially in cases where objects consist of repeating patterns, such as letters in logotypes, where potentially useful keypoints can get rejected. In this paper we propose a geometry-based approach for filtering out nondiscriminative keypoints. Our approach is not affected by repeating patterns and filters out non discriminative keypoints by means of prelearned geometry constraints. We evaluate our proposed method on a challenging dataset depicting logotypes in real-world environments under strong illumination and viewpoint changes.