OBJECT CLASSIFICATION WITH CONSTRAINED MULTIPLE INSTANCE SUPPORT VECTOR MACHINE
First Claim
Patent Images
1. A computer implemented method of classifying a digital image of an object, the method comprising:
- a) receiving a digital image of an object to be classified with a processor; and
b) classifying the digital image with a constrained multiple-instance support vector machine (MI-SVM) classifier, the constrained MI-SVM classifier having been automatically trained using a plurality of training images, the training images including a plurality of object types from a plurality of viewpoints, each training image including an image of an object associated with one of the plurality of object types and one of the plurality of object viewpoints, an associated object type label and an associated viewpoint label, the constrained MI-SVM classifier trained by sampling each training image to generate a bag of image regions associated with each training image, discovering s discriminative image region associated with each training image, and generating a collection of discriminative image regions for each of the plurality of object types and each of the plurality of viewpoints.
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Abstract
This disclosure provides method and systems of classifying a digital image of an object. Specifically, according to one exemplary embodiment, an object classifier is trained using a constrained MI-SVM (multiple instance-support vector machine) approach whereby training images of objects are sampled to generate a collection of image regions associated with an object type and viewpoint, and the classifier is trained to determine an appropriate mid-level representation of the training image which is discriminative.
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Citations
25 Claims
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1. A computer implemented method of classifying a digital image of an object, the method comprising:
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a) receiving a digital image of an object to be classified with a processor; and b) classifying the digital image with a constrained multiple-instance support vector machine (MI-SVM) classifier, the constrained MI-SVM classifier having been automatically trained using a plurality of training images, the training images including a plurality of object types from a plurality of viewpoints, each training image including an image of an object associated with one of the plurality of object types and one of the plurality of object viewpoints, an associated object type label and an associated viewpoint label, the constrained MI-SVM classifier trained by sampling each training image to generate a bag of image regions associated with each training image, discovering s discriminative image region associated with each training image, and generating a collection of discriminative image regions for each of the plurality of object types and each of the plurality of viewpoints. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. An image processing system comprising:
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a controller configured to receive a digital image of a vehicle including one of a plurality of vehicle types, the controller configured to execute instructions to perform a method of classifying the digital image of the vehicle as one of the plurality of vehicle types, the method comprising; a) receiving a digital image of an object to be classified; and b) with a processor, classifying the digital image with a constrained multiple-instance support vector machine (MI-SVM) classifier, the constrained MI-SVM classifier having been automatically trained using a plurality of training images, the training images including a plurality of object types from a plurality of viewpoints, each training image including an image of an object associated with one of the plurality of object types and one of the plurality of object viewpoints, an associated object type label and an associated viewpoint label, the constrained MI-SVM classifier trained by sampling each training image to generate a bag of image regions associated with each training image, discovering s discriminative image region associated with each training image, and generating a collection of discriminative image regions for each of the plurality of object types and each of the plurality of viewpoints. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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23. A computer implemented method of training a constrained multiple instance support vector machine (MI-SVM) classifier to classify digital images of an object, the method comprising:
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a) inputting to the constrained MI-SVM classifier a plurality of training images, each training image including an object image associated with one of a plurality of object types and one of a plurality of object viewpoints, an associated object type label and associated viewpoint label; b) the constrained MI-SVM classifier sampling each training image to generate a plurality of image regions providing a bag of image regions associated with each training image; c) the constrained MI-SVM classifier processing the bags of image regions associated with each training image to discover a discriminative image region associated with each training image, and generate a collection of discriminative image regions for each of the plurality of object types and each of the plurality of viewpoints. - View Dependent Claims (24, 25)
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Specification