1. A method, comprising: receiving, using one or more data processors, a digitized version of a two-dimensional (2D) image containing a 2D representation of a three-dimensional (3D) object; obtaining, using the one or more data processors, visual information from said digitized version of said 2D image; and classifying, using the one or more data processors, said 2D image based on a ratio of a plurality of graphical probability models using said visual information; wherein said classifying includes: applying a view-based classifier to said 2D image, wherein said classifier includes a plurality of sub-classifiers; computing a sum of log-likelihood ratios for each of said plurality of sub-classifiers, wherein said log-likelihood ratio is a ratio of two graphical probability models; wherein a graphical probability model comprises a probability distribution over a set of variables where statistical independence and conditional statistical independence exist among various combinations of the variables, wherein the graphical probability model is a probability distribution representation derived from statistical dependencies among image input variables.
1. A method, comprising: receiving, using one or more data processors, a digitized version of a two-dimensional (2D) image containing a 2D representation of a three-dimensional (3D) object; obtaining, using the one or more data processors, visual information from said digitized version of said 2D image; and classifying, using the one or more data processors, said 2D image based on a ratio of a plurality of graphical probability models using said visual information; wherein said classifying includes: applying a view-based classifier to said 2D image, wherein said classifier includes a plurality of sub-classifiers; computing a sum of log-likelihood ratios for each of said plurality of sub-classifiers, wherein said log-likelihood ratio is a ratio of two graphical probability models; wherein a graphical probability model comprises a probability distribution over a set of variables where statistical independence and conditional statistical independence exist among various combinations of the variables, wherein the graphical probability model is a probability distribution representation derived from statistical dependencies among image input variables. 4. The method of claim 1 , wherein each of said plurality of graphical probability models is a Bayesian Network (BN).