Optimizing multi-class image classification using patch features
First Claim
1. A computer storage media encoded with instructions that, when executed by a processor, configure a computer to perform acts comprising:
- accessing a plurality of weakly supervised images;
extracting one or more patches from individual weakly supervised images of the plurality of weakly supervised images;
extracting patch-based features from the one or more patches;
arranging individual patches into a plurality of clusters based at least in part on the patch-based features;
removing at least some of the individual patches from at least one cluster of the plurality of clusters based at least in part on similarity values representative of a similarity between ones of the individual patches arranged in the at least one cluster; and
training a classifier for at least one label of a plurality of labels based at least in part on the plurality of clusters, comprising;
extracting new patch-based features from remaining individual patches of the at least one cluster; and
training the classifier based at least in part on the new patch-based features.
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Abstract
Optimizing multi-class image classification by leveraging patch-based features extracted from weakly supervised images to train classifiers is described. A corpus of images associated with a set of labels may be received. One or more patches may be extracted from individual images in the corpus. Patch-based features may be extracted from the one or more patches and patch representations may be extracted from individual patches of the one or more patches. The patches may be arranged into clusters based at least in part on the patch-based features. At least some of the individual patches may be removed from individual clusters based at least in part on determined similarity values that are representative of similarity between the individual patches. The system may train classifiers based in part on patch-based features extracted from patches in the refined clusters. The classifiers may be used to accurately and efficiently classify new images.
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Citations
20 Claims
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1. A computer storage media encoded with instructions that, when executed by a processor, configure a computer to perform acts comprising:
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accessing a plurality of weakly supervised images; extracting one or more patches from individual weakly supervised images of the plurality of weakly supervised images; extracting patch-based features from the one or more patches; arranging individual patches into a plurality of clusters based at least in part on the patch-based features; removing at least some of the individual patches from at least one cluster of the plurality of clusters based at least in part on similarity values representative of a similarity between ones of the individual patches arranged in the at least one cluster; and training a classifier for at least one label of a plurality of labels based at least in part on the plurality of clusters, comprising; extracting new patch-based features from remaining individual patches of the at least one cluster; and training the classifier based at least in part on the new patch-based features. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system comprising:
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one or more processing units; and computer storage media storing instructions that when executable by the one or more processing units cause the system to perform operations comprising; accessing a plurality of weakly supervised images; extracting one or more patches from individual weakly supervised images of the plurality of weakly supervised images; extracting patch-based features from the one or more patches; arranging individual patches into a plurality of clusters based at least in part on the patch-based features; removing at least some of the individual patches from at least one cluster of the plurality of clusters based at least in part on similarity values representative of similarity between ones of the individual patches arranged in the at least one cluster; and training a classifier for at least one label of a plurality of labels based at least in part on the plurality of clusters, comprising; extracting new patch-based features from remaining individual patches of the at least one cluster; and training the classifier based at least in part on the new patch-based features. - View Dependent Claims (11, 12, 13, 14)
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15. A computer-implemented method comprising:
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accessing a plurality of weakly supervised images; extracting one or more patches from individual weakly supervised images of the plurality of weakly supervised images; extracting patch-based features from the one or more patches; arranging individual patches into a plurality of clusters based at least in part on the patch-based features; removing at least some of the individual patches from at least one cluster of the plurality of clusters based at least in part on similarity values representative of similarity between ones of the individual patches arranged in the at least one cluster; and training a classifier for at least one label of a plurality of labels based at least in part on the plurality of clusters, comprising; extracting new patch-based features from remaining individual patches of the at least one cluster; and training the classifier based at least in part on the new patch-based features. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification