1. A method comprising: receiving, at a computing device, a plurality of candidate images; using, via the computing device, a learned probabilistic composition model to divide each candidate image in the plurality of candidate images into a most probable rectangular object region and a background region, wherein the most probable rectangular object region has a maximal composition score from possible composition scores computed according to the composition model for possible divisions of the candidate image into object and background regions, each possible composition score is based upon at least one image feature cue computed over the object and background regions, and the composition model is trained on a set of images independent of the plurality of candidate images; ranking, via the computing device, the plurality of candidate images according to the maximal composition score of the most probable rectangular object region of each image determined using the learned probabilistic composition model; removing, via the computing device, non-discriminative images from the plurality of candidate images; clustering, via the computing device, a plurality of highest-ranked images from the plurality of candidate images ranked according to the maximal composition score of the most probable rectangular object region of each image determined using the learned probabilistic composition model to form a plurality of clusters, wherein each cluster includes a plurality of images selected from the plurality of highest-ranked images and having similar object regions according to a feature match score; selecting, via the computing device, a representative image from each cluster as an iconic image representative of an object category; and causing, via the computing device, display of the iconic image.