12. The method of claim 1 , further comprising selecting one or more visual concepts for training the visual-concept recognition system, wherein the selecting comprises: accessing distribution data that classifies visual-media items in a sample set as including one or more categories of visual concepts; estimating, based on the distribution data, projected frequencies for each of the one or more categories of visual concepts in a larger set of visual-media items, wherein each projected frequency describes a frequency of occurrence of the respective category of visual concepts in the larger set that are predicted to include one or more visual concepts of the respective category of visual concepts; and determining, based on the projected frequencies, whether there exists a representative number of n-gram associations with one or more visual concepts of each category of visual concepts.
13. The method of claim 12 , further comprising receiving inputs from one or more human evaluators specifying whether or not each of one or more of the visual concepts is capable of being described by n-grams.