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This paper investigates the use of a nonparametric regularization energy term for devising a example-based rendering and segmentation technique. We have stated this problem in the multiresolution energy minimization framework and exploited the multiscale structure proposed by Wei and Levoy for the texture synthesis problem. In this nonparametric energy minimization framework, we also propose a computationally efficient coarse-to-fine recursive optimization method to minimize the cost function related to this hierarchical model. In this context, the formulation of our example-based regularization term also allows to directly infer an intuitive dissimilarity measure between two contour shapes. This measure is herein exploited to define an efficient shape descriptor for the contour-based shape recognition and indexing problem.