Glocal shape context descriptor in cluttered images

Shape context has been proven to be an effective method for both local feature matching and global context description. In this paper, we propose a method to build a glocal shape context descriptor in cluttered images. By using the proposed keypoint centered multiple scale edge detection (KMSED) method, glocal shape context encodes fine-scale edges in the keypoint center region while coarse-scale edges in the outer region. In this way, local and global image information are encoded at the same time into a 68 dimension feature vector. Experiments show that the proposed glocal shape context makes significant enhancement over the local shape context descriptor and outperforms SIFT under severe illumination change and high JPEG compression.