Partial Object Matching with Shapeme Histograms

Ying Shan, Harpreet S. Sawhney, Bogdan Matei, Rakesh Kumar

Abstract: Histogram of shape signature or prototypical shapes, called shapemes, have been used effectively in previous work for 2D/3D shape matching & recognition. We extend the idea of shapeme histogram to recognize partially observed query objects from a database of complete model objects. We propose to represent each model object as a collection of shapeme histograms, and match the query histogram to this representation in two steps: (i) compute a constrained projection of the query histogram onto the subspace spanned by all the shapeme histograms of the model, and (ii) compute a match measure between the query histogram and the projection. The first step is formulated as a constrained optimization problem that is solved by a sampling algorithm. The second step is formulated under a Bayesian framework where an implicit feature selection process is conducted to improve the discrimination capability of shapeme histograms. Results of matching partially viewed range objects with a 243 model database demonstrate better performance than the original shapeme histogram matching algorithm and other approaches.