Mr. B. Srinidhi, Mr. E. Suneel / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 4, Jul-Aug 2013, pp.2510-2516 VII. CONCLUSION In this paper we have presented the Shape Boltzmann Machine, a strong generative model of object shape. The SBM is based on the general DBM architecture, a form of undirected graphical model that makes heavy use of latent variables to model high-order dependencies between the observed variables. We believe that the combination of (a) carefully chosen connectivity and capacity constraints, along with (b) a hierarchical architecture, and (c) a training procedure that allows for the joint optimization of the full model, is key to the success of the SBM.
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These ingredients allow the SBM to learn high quality probability distributions over object shapes from small datasets, consisting of just a few hundred training images. The learned models are convincing in terms of both realism of samples from the distribution and generalization to new examples of the same shape class. Without making use of specialist knowledge about the particular shapes the model develops a natural representation with some separation of concerns across layers.
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