Discrimination and description of repetitive patterns for enhancing the performance of feature-based recognition

摘要

Conventional object retrieval or recognition methods based on feature matching sometimes fail when an object contains repetitive patterns, because features from repetitive patterns are too similar to each other. Specifically, when there arise many similar features in a query object due to repetitive patterns, they are usually not matched to the ones at the same positions of the reference object. Hence, the matching pairs between the query and reference image do not appear regular and thus homography estimation fails. In case when we use “nearest neighbor distance ratio” as a matching criterion, where enough distinction between the matched pairs should be secured, matching also fails due to similarity of features. In this paper, we propose a new feature matching strategy to alleviate this problem by discriminating repetitive patterns from the other salient ones and also by developing a way of utilizing the patterns for robust feature matching. Specifically, we first apply a conventional feature extraction method to a given image. Then we cluster features based on their similarity, i.e., we design a classifier that tells whether a feature is from a repetitive pattern or from a salient structure. For the effective use of repetitive patterns, we define a new descriptor based on support vector data description (SVDD) for describing clusters of similar features. In other words, a set of features from a pattern is defined to be a new feature in terms of its center and radius. For object recognition, the homography is found over the salient features by excluding repetitive features at first, which is then validated and refined by the repetitive patterns. The proposed method is tested with examples of matching buildings with repetitive patterns, and it is shown to be robuster and more reliable than the conventional methods.