Discrimination and description of repetitive patterns for enhancing object recognition performance

Objects with repetitive patterns are not well recognized by SIFT/SURF based matching because the features from those patterns are too similar and thus it is often difficult to find the homography of matched pairs. In this paper, we propose a new feature matching strategy to alleviate this problem by differentiating repetitive patterns from the other salient ones and also by developing a way of utilizing the patterns for robust feature matching. Specifically, we develop a classifier that tells whether the features are from repetitive patterns or salient features, based on mean shift clustering followed by support vector data description. Then the homography is found over the salient features by excluding the repetitive features at first, which is then validated and refined by the patterns. The proposed method is tested with the examples of matching the buildings with repeating patterns, and it is shown to be robuster and more reliable than the conventional ones.