System and method for object class localization and semantic class based image segmentation
First Claim
1. An automated image processing method comprising:
- with a processor;
extracting a plurality of patches of an input image;
for each patch, extracting at least one high level feature based on its low level representation and a generative model built from low level features;
for each patch, and for at least one object class from a set of object classes, computing a relevance score for the patch based on the at least one high level feature and the output of at least one patch classifier;
for at least some of the pixels of the image, computing a relevance score for the at least one object class based on the patch scores; and
assigning an object class label to each of the pixels based on the computed relevance score for the at least one object class.
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Abstract
An automated image processing system and method are provided for class-based segmentation of a digital image. The method includes extracting a plurality of patches of an input image. For each patch, at least one feature is extracted. The feature may be a high level feature which is derived from the application of a generative model to a representation of low level feature(s) of the patch. For each patch, and for at least one object class from a set of object classes, a relevance score for the patch, based on the at least one feature, is computed. For at least some or all of the pixels of the image, a relevance score for the at least one object class based on the patch scores is computed. An object class is assigned to each of the pixels based on the computed relevance score for the at least one object class, allowing the image to be segmented and the segments labeled, based on object class.
64 Citations
21 Claims
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1. An automated image processing method comprising:
with a processor; extracting a plurality of patches of an input image; for each patch, extracting at least one high level feature based on its low level representation and a generative model built from low level features; for each patch, and for at least one object class from a set of object classes, computing a relevance score for the patch based on the at least one high level feature and the output of at least one patch classifier; for at least some of the pixels of the image, computing a relevance score for the at least one object class based on the patch scores; and assigning an object class label to each of the pixels based on the computed relevance score for the at least one object class. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A computer program product comprising a non-transitory recording medium that stores instructions which, when executed by a computer, perform an image processing method comprising:
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extracting a plurality of patches of an input image; for each patch, extracting at least one high level feature based on its low level representation and a generative model built from low level features; for each patch, and for at least one object class from a set of object classes, computing a relevance score for the patch based on the at least one high level feature and the output of at least one patch classifier; for at least some of the pixels of the image, computing a relevance score for the at least one object class based on the patch scores; and assigning an object class label to pixels of the image, based on the computed relevance score for the at least one object class.
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18. An automated image processing system comprising:
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a patch extractor which extracts patches of an input image; a low level feature extractor which extracts, for each patch, a low level feature; a high level feature extractor which extracts, for each patch, a high level feature based on the low level feature and a generative model built on low level features; a classifier system, configured for classifying the patch, based on the high level feature, for each of a set of object classes; a scoring component which for each patch, and for at least one object class from a set of object classes, computes a relevance score for the patch based on the classifier and, for at least some of the pixels of the image, computes a relevance score for the at least one object class based on the patch scores; and a labeling component assigns an object class to each of the pixels based on the computed relevance score for the at least one object class.
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19. A digital image processing method comprising:
with a processor; for an input image, extracting patches in the image; from each patch, extracting a low-level representation comprising a feature vector; for each patch, using its low-level representation and a generative model to extract a high-level representation; for each patch and each class, computing a relevance score based on the high-level representation and a patch classifier; for each pixel and each class, computing a relevance score based on the patch scores; and for each pixel, take a decision based on the class scores. - View Dependent Claims (20, 21)
Specification