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Keypoint Extraction and Selection for Object Recognition

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.