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.