Automatic Sparsity-Aware Recognition for Keypoint Detection

We present a novel Sparsity-Aware Keypoint detector (SAKD) to localize a set of discriminative keypoints via optimization of group-sparse coding. Unlike most of current handcrafted keypoint detectors that are limited by the manually defined local structures, the proposed method has the capacity to allow flexibility for exploiting diverse structures with the combination of visual atoms from a vocabulary. Another key valuable attribute is that its group-sparsity nature concentrates on discovering sharable structural patterns across keypoints within an image jointly. This main merit facilitates to localize repeatable keypoints and resists against distractors when image undergoes various transformations. Extensive experiments on four challenging benchmark datasets demonstrate that the proposed method achieves favorable performances compared with state-of-the-art in literature.