FINDING ICONIC IMAGES
First Claim
1. A computer implemented method of finding iconic images for a given object category, the 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 each image;
removing non-discriminative images from the plurality of candidate images;
clustering a plurality of highest-ranked images from the plurality of candidate images to form a plurality of clusters, wherein each cluster includes a plurality of images having similar object regions according to a feature match score;
selecting a representative image from each cluster as an iconic image of the object category; and
causing display of the iconic image.
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Abstract
Iconic images for a given object or object category may be identified in a set of candidate images by using a learned probabilistic composition model to divide each candidate image into a most probable rectangular object region and a background region, ranking the candidate images according to the maximal composition score of each image, removing non-discriminative images from the candidate images, clustering highest-ranked candidate images to form clusters, wherein each cluster includes images having similar object regions according to a feature match score, selecting a representative image from each cluster as an iconic image of the object category, and causing display of the iconic image. The composition model may be a Naïve Bayes model that computes composition scores based on appearance cues such as hue, saturation, focus, and texture. Iconic images depict an object or category as a relatively large object centered on a clean or uncluttered contrasting background.
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Citations
42 Claims
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1. A computer implemented method of finding iconic images for a given object category, the method comprising:
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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 each image; removing non-discriminative images from the plurality of candidate images; clustering a plurality of highest-ranked images from the plurality of candidate images to form a plurality of clusters, wherein each cluster includes a plurality of images having similar object regions according to a feature match score; selecting a representative image from each cluster as an iconic image of the object category; and causing display of the iconic image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A computer system for finding iconic images of a given object category, the system comprising:
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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 each image; removing non-discriminative images from the plurality of candidate images; clustering a plurality of highest-ranked images from the plurality of candidate images to form a plurality of clusters, wherein each cluster includes a plurality of images having similar object regions according to a feature match score; selecting a representative image from each cluster as an iconic image of the object category; and causing display of the iconic image. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28)
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29. A computer readable medium comprising computer-executable instructions for finding iconic images of a given object category, the instructions for:
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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 each image; removing non-discriminative images from the plurality of candidate images; clustering a plurality of highest-ranked images from the plurality of candidate images to form a plurality of clusters, wherein each cluster includes a plurality of images having similar object regions according to a feature match score; selecting a representative image from each cluster as an iconic image of the object category; and causing display of the iconic image. - View Dependent Claims (30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42)
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Specification