In order to improve the performance of affine invariant detectors, approaches that combine different keypoint extraction methods can be found in literature. However, such a combining has two major drawbacks: a high computational cost in matching similar objects, and a large number of false positives because only a relatively small subset of those keypoints is really discriminative. In this paper we propose a method to overcome these limitations: First we combine different keypoint extractors in order to obtain a large set of possible interest points for a given object. Then, a two step filtering approach is applied: First, a reduction using a spatial criterion to reject points that are close in a specified neighborhood, and second, filtering based on the information entropy in order to select only a small subset of keypoints that offer the highest information content. A qualitative analysis of this method is presented.
['Maja Rudinac', 'Boris Lenseigne', 'Pieter P. Jonker']
Keypoint extraction and selection for object recognition