A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching

—This paper presents a novel probabilistic approach to hierarchical, exemplar-based shape matching. No feature correspondence is needed among exemplars, just a suitable pairwise similarity measure. The approach uses a template tree to efficiently represent and match the variety of shape exemplars. The tree is generated offline by a bottom-up clustering approach using stochastic optimization. Online matching involves a simultaneous coarse-to-fine approach over the template tree and over the transformation parameters. The main contribution of this paper is a Bayesian model to estimate the a posteriori probability of the object class, after a certain match at a node of the tree. This model takes into account object scale and saliency and allows for a principled setting of the matching thresholds such that unpromising paths in the tree traversal process are eliminated early on. The proposed approach was tested in a variety of application domains. Here, results are presented on one of the...

• Type: Journal
• Year: 2007
• Where: PAMI
• Authors: Dariu Gavrila
• Keywords: Bottom-up Clustering Approach, PAMI 2007, Simultaneous Coarse-to-fine Approach, Template Tree