In this paper, we develop an expectation propagation learning framework for the inverted Dirichlet (ID) and Dirichlet mixture models. The main goal is to implement an algorithm to recognize 3D objects. Those objects are in our case from a view-based 3D models database that we have assembled. Following specific rules determined by analyzing the results of our tests, we have been able to get promising recognition rates. Experimental results are presented with different object classes by comparing recognition rates and confidence levels according to different tuning parameters.
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