1. A method of classifying the material type of an unknown material into a probability distribution of multiple predetermined material types by using a collection of plural predetermined material classifiers, wherein each material type of the multiple predetermined material types is associated with a corresponding best performing classifier from the collection of plural predetermined material classifiers by a predesignated stored association, the method comprising the steps of: applying, using a processor, the plural material classifiers to the unknown material to obtain a list of candidate material types; looking up, using the processor, a list of potential best performing classifiers using the list of candidate material types as a reference into the predesignated stored association; and assigning, using the processor, a respective probability that the unknown material belongs to a material type based on the list of potential best performing classifiers, wherein applying comprises obtaining one or more feature vectors for the unknown material and inputting a corresponding one of the feature vectors into each of the plural material classifiers so as to obtain an output representative of material type from each of the plural material classifiers.
1. A method of classifying the material type of an unknown material into a probability distribution of multiple predetermined material types by using a collection of plural predetermined material classifiers, wherein each material type of the multiple predetermined material types is associated with a corresponding best performing classifier from the collection of plural predetermined material classifiers by a predesignated stored association, the method comprising the steps of: applying, using a processor, the plural material classifiers to the unknown material to obtain a list of candidate material types; looking up, using the processor, a list of potential best performing classifiers using the list of candidate material types as a reference into the predesignated stored association; and assigning, using the processor, a respective probability that the unknown material belongs to a material type based on the list of potential best performing classifiers, wherein applying comprises obtaining one or more feature vectors for the unknown material and inputting a corresponding one of the feature vectors into each of the plural material classifiers so as to obtain an output representative of material type from each of the plural material classifiers. 7. The method according to claim 1 , wherein the collection of plural material classifiers includes a classifier based on a bidirectional surface scattering reflectance distribution function, and the corresponding feature vector for such classifier is representative of a subsurface scattering property of the unknown material.