Shape Description Using Gradient Vector Field Histograms

摘要

We present a novel approach to shape representation that describes a shape using a set of histograms derived at salient points within the shape. A computationally efficient multiresolution pyramidal framework is used to generate a dense gradient vector field whose characteristics can be altered through the use of a scale parameter α. This parameter regulates the proportion of low and high spatial frequency components used in creating the vector field and can be set such that minor boundary distortions do not significantly change the representation of the shape. Local maximas of the directional disparity measure in the vector field are used for locating shape axes, from where polar sampling of the vector field is then used to build scale and rotational invariant histograms that describes subparts of the shape. A saliency measure based on the size of a part is introduced to provide appropriate weighting to each part during the shape matching process. Experimental results involving silhouettes images are presented to demonstrate the effectiveness of the proposed gradient vector field histograms for similarity-based shape retrieval.