Learning Hierarchical Shape Models from Examples

Abstract. We present an algorithm for automatically constructing a decompositional shape model from examples. Unlike current approaches to structural model acquisition, in which one-to-one correspondences among appearance-based features are used to construct an exemplar-based model, we search for many-tomany correspondences among qualitative shape features (multi-scale ridges and blobs) to construct a generic shape model. Since such features are highly ambiguous, their structural context must be exploited in computing correspondences, e often many-to-many. The result is a Marr-like abstraction hierarchy, in which a shape feature at a coarser scale can be decomposed into a collection of attached shape features at a finer scale. We systematically evaluate all components of our algorithm, and demonstrate it on the task of recovering a decompositional model of a human torso from example images containing different subjects with dissimilar local appearance.

• Type: Conference
• Year: 2005
• Where: EMMCVPR
• Authors: Alex Levinshtein, Cristian Sminchisescu, Sven J. Dickinson
• Keywords: Decompositional Shape Model, EMMCVPR 2005, Shape Features, Shape Model