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We present a part-based shape descriptor that incorporates both the description of the general shape form of each subpart and its geometric relationship with other connected parts. Associated with each descriptor is a saliency measure that weighs each part's visual significance. By incorporating this saliency measure into the shape matching process, we able to discriminate between shape forms as well as take boundary texture into consideration when computing shape similarity. This paper also describes a multi-resolution pyramidal framework for generating the required gradient vector field and vector field disparity map from which the shape descriptors, in the form of gradient vector field histograms, are derived. Experimental results involving silhouettes images are presented to demonstrate the various characteristics of the proposed shape descriptor, which includes its invariance to similarity transform and its ability to match composite shapes containing boundary noise and texture, limb articulation and occlusion.