1. A method of cross descriptor learning using unlabeled exemplars, said method comprising of the steps of: a) extracting descriptors for each of a plurality of unlabeled exemplars, each extracted descriptor being a representation of a corresponding one of said plurality of unlabeled exemplars; b) automatically generating labels from said each extracted descriptor, said each extracted descriptor being used to automatically generate labels for other ones of said descriptors; c) developing a predictor for said each extracted descriptor from generated said labels; and d) combining predictions across predictors, labels being generated from combined said predictions for each of said unlabeled exemplars.
1. A method of cross descriptor learning using unlabeled exemplars, said method comprising of the steps of: a) extracting descriptors for each of a plurality of unlabeled exemplars, each extracted descriptor being a representation of a corresponding one of said plurality of unlabeled exemplars; b) automatically generating labels from said each extracted descriptor, said each extracted descriptor being used to automatically generate labels for other ones of said descriptors; c) developing a predictor for said each extracted descriptor from generated said labels; and d) combining predictions across predictors, labels being generated from combined said predictions for each of said unlabeled exemplars. 2. A method as in claim 1 , wherein the step (b) of automatically generating labels comprises providing said unlabeled exemplars to a label provider service.