Finding iconic images

A computer system comprising:

• a memory for storing computer-executable instructions;
• and
• a processor for executing the instructions, the instructions for:
• receiving a plurality of candidate images;
• using 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 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 non-discriminative images from the plurality of candidate images;
• clustering 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 a representative image from each cluster as an iconic image representative of an object category;
• and
• causing display of the iconic image.

A computer readable storage medium comprising computer-executable instructions tangibly stored thereon, which when executed by a processor, perform a method comprising:

• receiving a plurality of candidate images;
• using 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 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 non-discriminative images from the plurality of candidate images;
• clustering 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 a representative image from each cluster as an iconic image representative of the object category;
• and
• causing display of the iconic image.