Learning part-based models of objects
First Claim
1. A computer implemented method for learning part-based object models from a set of digital representations of images, the method comprising:
- receiving a set of digital representations of images, each image having at least one object;
for each image;
extracting one or more shape features from the image;
extracting one or more appearance features from the image;
generating one or more shape models of the object based on the shape features, a shape model corresponding to a part of the object, generating a shape model of the object comprising;
generating one or more histogram of oriented gradient (HOG) cells for a number of pixel of the image; and
grouping two or more HOG cells into a HOG bundle based on similarity of orientations of the HOG cells with respect to the maximum magnitude of orientations of the HOG cells;
computing an appearance model for each shape model of the object based on the appearance features;
selecting one or more shape models as reference shape models of the object;
selecting one or more appearance models as reference appearance models of the object; and
storing the reference shape models of the images; and
storing the reference appearance models of the images.
1 Assignment
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Accused Products
Abstract
A system and method are provided for learning part-based object models during a learning phase from training images and applying the learned object models to an input image during runtime. The learned part-based object models are augmented by appearance-based models of the objects. The part-based object models correspond to the shapes of the parts of an object. The appearance-based models provide additional appearance cues to the object models for object classification. The approach to learning part-based object models has the capability of learning object models without using viewpoint labels of the objects. The learning is also invariant to scale and in-plane rotation of the objects.
10 Citations
26 Claims
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1. A computer implemented method for learning part-based object models from a set of digital representations of images, the method comprising:
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receiving a set of digital representations of images, each image having at least one object; for each image; extracting one or more shape features from the image; extracting one or more appearance features from the image; generating one or more shape models of the object based on the shape features, a shape model corresponding to a part of the object, generating a shape model of the object comprising; generating one or more histogram of oriented gradient (HOG) cells for a number of pixel of the image; and grouping two or more HOG cells into a HOG bundle based on similarity of orientations of the HOG cells with respect to the maximum magnitude of orientations of the HOG cells; computing an appearance model for each shape model of the object based on the appearance features; selecting one or more shape models as reference shape models of the object; selecting one or more appearance models as reference appearance models of the object; and storing the reference shape models of the images; and storing the reference appearance models of the images. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer system for learning part-based object models from a set of digital representations of images, the system comprising:
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a non-transitory computer-readable storage medium storing executable computer program instructions; and a computer processor executing the computer program instructions to perform steps comprising; receiving a set of digital representations of images, each image having at least one object; and for each image; extracting one or more shape features from the image; extracting one or more appearance features from the image; generating one or more shape models of the object based on the shape features, a shape model corresponding to a part of the object, generating a shape model comprising; generating one or more histogram of oriented gradient (HOG) cells for a number of pixel of the image; and grouping two or more HOG cells into a HOG bundle based on similarity of orientations of the HOG cells with respect to the maximum magnitude of orientations of the HOG cells; computing an appearance model for each shape model of the object based on the appearance features; and selecting one or more shape models as reference shape models of the object; selecting one or more appearance models as reference appearance models of the object; and storing the reference shape models of the images; and storing the reference appearance models of the images. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A non-transitory computer-readable storage medium storing executable computer program instructions for learning part-based object models from a set of digital representations of images, the computer program instructions comprising instructions for:
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receiving a set of digital representations of images, each image having at least one object; for each image; extracting one or more shape features from the image; extracting one or more appearance features from the image; generating one or more shape models of the object based on the shape features, a shape model corresponding to a part of the object, generating one or more shape models comprising; generating one or more histogram of oriented gradient (HOG) cells for a number of pixel of the image; and grouping two or more HOG cells into a HOG bundle based on similarity of orientations of the HOG cells with respect to the maximum magnitude of orientations of the HOG cells; computing an appearance model for each shape model of the object based on the appearance features; selecting one or more shape models as reference shape models of the object; and selecting one or more appearance models as reference appearance models of the object; and storing the reference shape models and appearance models of the images; and storing the reference appearance models of the images. - View Dependent Claims (18, 19, 20)
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21. A computer implemented method for classifying an object contained in digital representation of an image, the method comprising:
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receiving one or more reference shape models; receiving one or more reference appearance models; generating one or more shape models of the object based on shape features extracted from the image, a shape model corresponding to a part of the object, generating a shape model comprising; generating one or more histogram of oriented gradient (HOG) cells for a number of pixel of the image; and grouping two or more HOG cells into a HOG bundle based on similarity of orientations of the HOG cells with respect to the maximum magnitude of orientations of the HOG cells; comparing the generated shape models with the reference shape models; selecting one or more candidate shape models of the object based on the comparison; augmenting each candidate shape model with a corresponding appearance model of the object; and determining classification of the object based on the augmented candidate shape models. - View Dependent Claims (22)
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23. A computer system for classifying an object contained in digital representation of an image, the system comprising:
a non-transitory computer-readable storage medium storing executable computer program instructions that when executed by a computer processor cause the computer processor to; receive one or more reference shape models; receive one or more reference appearance models; generate one or more shape models of the object based on shape features extracted from the image, a shape model corresponding to a part of the object, generating a shape model comprising; generating one or more histogram of oriented gradient (HOG) cells for a number of pixel of the image; and grouping two or more HOG cells into a HOG bundle based on similarity of orientations of the HOG cells with respect to the maximum magnitude of orientations of the HOG cells; compare the generated shape models with the reference shape models; select one or more candidate shape models of the object based on the comparison; augment each candidate shape model with a corresponding appearance model of the object; and determine classification of the object based on the augmented candidate shape models. - View Dependent Claims (24)
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25. A non-transitory computer-readable storage medium storing executable computer program instructions for classifying an object contained in digital representation of an image, the computer program instructions comprising instructions for:
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receiving one or more reference shape models; receiving one or more reference appearance models; generating one or more shape models of the object based on shape features extracted from the image, a shape model corresponding to a part of the object, generating one or more shape models comprising; generating one or more histogram of oriented gradient (HOG) cells for a number of pixel of the image; and grouping two or more HOG cells into a HOG bundle based on similarity of orientations of the HOG cells with respect to the maximum magnitude of orientations of the HOG cells; comparing the generated shape models with the reference shape models; selecting one or more candidate shape models of the object based on the comparison; augmenting each candidate shape model with a corresponding appearance model of the object; and determining classification of the object based on the augmented candidate shape models. - View Dependent Claims (26)
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Specification