On Bin Configuration of Shape Context Descriptors in Human Silhouette Classification

Shape context descriptors have been a valuable tool in shape description since their introduction. In this paper we examine the performance of shape context descriptors in the presence of noisy human silhouette data. Shape context descriptors have been shown to be robust to Gaussian noise in the task of shape matching. We implement four different configurations of shape context by altering the spacing of the histogram bins and then test the performance of these configurations in the presence of noise. The task used for these tests is recognition of body part shapes in human silhouettes. The noise in human silhouettes is principally from three sources: the noise from errors in silhouette segmentation, noise from loose clothing and noise from occlusions. We show that in the presence of this noise a newly proposed spacing for the shape context histogram bins has the best performance.