A SEMI-HANDCRAFTED KEYPOINT DETECTOR WITH DISCRIMINATIVE FEATURE ENCODING

Most previous handcrafted keypoint methods focus on designing specific structural patterns using human-defined knowledge. These methods, however, ignore the fact that whether they have enough flexibility to harvest diverse local structures. Recently, the semi-handcrafted approaches based on sparse coding have emerged as a new trend of alleviating the above issue. And yet, the intrinsic relationships of keypoints have not been explored actively, which may lead to the ambiguity of feature codes for further analysis. To tackle this problem, in this paper, we introduce a novel semi-handcrafted keypoint detector through a scheme of discriminative feature representations (SDFR). Specifically, we cast keypoint detection as an optimization problem on a visual dictionary that explicitly models the visual relationships of feature points to preserve the consistency of similar features and distance dissimilar ones. Further, we propose an iterative solver for the SDFR model. Experimental results on challenge benchmarks demonstrate that the proposed method performs favorably against state-of-the-art in literature.