5. A computer program product comprised of software instructions on a non-transitory computer-readable medium, where execution of the software instructions using a computer results in performing operations comprising: for a subject category of interest having a plurality N of sub-categories, where N is an integer greater than one, training over a first set of images a plurality of image classifiers to yield a plurality of pre-trained classifiers (C 1 . . . C N ), where each individual one of the pre-trained classifiers is trained to recognize one sub-category of the plurality of sub-categories; obtaining a second set of images (i=1 . . . M) from at least one social media website that a person has shared/posted/pinned or otherwise expressed an interest in, where M represents a total number of images in the second set of images; applying at least some of the pre-trained classifiers (C 1 . . . C N ) to each image i in the second set of images; obtaining a classification score vector {right arrow over (S)} i =[s 1 . . . , s N ] for image i(i=1 . . . M), where s j indicates a classification score output from a classifier C j by applying it to image i; aggregating the M classification score vectors over all of the images of the second set of images; deriving a data construct that is descriptive of a preference profile of the person with respect to the sub-categories of the category of interest as a probability distribution over the N sub-categories; and characterizing by how much the person's profile differs from an average profile of a group of persons by assigning the person to a segment, where P a represents a probability vector for the person, and calculating an average P segment _ i for each segment i, where a degree to which the person is a member of the segment i is a dot product of the two vectors: 
 P a ·P segment _ i /(| P a ||P segment _ i |).
6. The computer program product of claim 5 , where the category is fashion, and where the sub-categories are each a particular fashion style.