16. An apparatus for generating classifier data structures for classifying graphical objects comprising: a sample imager configured to generate a stream of images; a region identifier configured to: detect graphical objects within images included in the stream of images; and define bounding regions for each of the detected graphical objects, each of the bounding regions encompassing one graphical object of the detected the graphical objects, wherein a border of each of the bounding regions is located a predetermined distance from segments of a corresponding graphical object; and a training processor configured to: identify one or more candidate features within at least one of the bounding regions defined for the graphical objects, each of the one or more candidate features defined by pixels within a corresponding bounding region; compare each of the one or more candidate features to feature definitions defining characteristics of one or more features detectable within one or more graphical objects; determine probabilities that each of the one or more candidate features satisfies a feature definition; and generating classifier data structures based, at least in part, on the probabilities.
16. An apparatus for generating classifier data structures for classifying graphical objects comprising: a sample imager configured to generate a stream of images; a region identifier configured to: detect graphical objects within images included in the stream of images; and define bounding regions for each of the detected graphical objects, each of the bounding regions encompassing one graphical object of the detected the graphical objects, wherein a border of each of the bounding regions is located a predetermined distance from segments of a corresponding graphical object; and a training processor configured to: identify one or more candidate features within at least one of the bounding regions defined for the graphical objects, each of the one or more candidate features defined by pixels within a corresponding bounding region; compare each of the one or more candidate features to feature definitions defining characteristics of one or more features detectable within one or more graphical objects; determine probabilities that each of the one or more candidate features satisfies a feature definition; and generating classifier data structures based, at least in part, on the probabilities. 19. The apparatus of claim 16 , wherein training processor is further configured to update one or more classifier data structures based on feedback received from client devices.