Computer vision system and method employing hierarchical object classification scheme
First Claim
1. A method for classifying an object in image data, comprising:
- detecting an object in said image data;
classifying said object using a hierarchical object classification scheme defined by providing a candidate class for the detected object with an increasing degree of specificity as a hierarchy of classes is traversed from a root node to a leaf node; and
outputting a class label to identify said candidate class to which the detected object corresponds to and a probability value to indicate a probability with which the detected object belongs to said candidate class.
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Accused Products
Abstract
A method and apparatus are disclosed for classifying objects using a hierarchical object classification scheme. The hierarchical object classification scheme provides candidate classes with an increasing degree of specificity as the hierarchy is traversed from the root node to the leaf nodes. Each node in the hierarchy has an associated classifier, such as a Radial Basis Function classifier, that determines a probability that an object is a member of the class associated with the node. The nodes of the hierarchical tree are individually trained by any learning technique, such as the exemplary Radial Basis Function Network, that uses appearance-based information of the objects under consideration to classify objects. A disclosed recognition scheme uses a decision criterion based upon recognition error to classify objects.
32 Citations
22 Claims
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1. A method for classifying an object in image data, comprising:
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detecting an object in said image data; classifying said object using a hierarchical object classification scheme defined by providing a candidate class for the detected object with an increasing degree of specificity as a hierarchy of classes is traversed from a root node to a leaf node; and outputting a class label to identify said candidate class to which the detected object corresponds to and a probability value to indicate a probability with which the detected object belongs to said candidate class. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for classifying an object in image data, comprising:
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detecting an object in said image data; evaluating a recognition error for said object for one or more nodes in a hierarchical object classification scheme, until a node having a lowest recognition error is identified, said hierarchical object classification scheme defined by providing a candidate class for the detected object with an increasing degree of specificity as a hierarchy of classes is traversed from a root node to a leaf node; and outputting a class label to identify said candidate class to which the detected object corresponds to and a probability value to indicate a probability with which the detected object belongs to said candidate class. - View Dependent Claims (12, 13, 14)
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15. A system for classifying an object in image data, comprising:
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a memory that stores computer-readable code; and a processor operatively coupled to said memory, said processor configured to implement said computer-readable code, said computer-readable code configured to; detect an object in said image data; classify said object using a hierarchical object classification scheme defined by providing a candidate class for the detected object with an increasing degree of specificity as a hierarchy of classes is traversed from a root node to a leaf node; and output a class label to identify said candidate class to which the detected object corresponds to and a probability value to indicate a probability with which the detected object belongs to said candidate class. - View Dependent Claims (16, 17, 18, 19)
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20. A system for classifying an object in image data, comprising:
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a memory that stores computer-readable code; and a processor operatively coupled to said memory, said processor configured to implement said computer-readable code, said computer-readable code configured to; detect an object in said image data; evaluate a recognition error for said object for one or more nodes in a hierarchical object classification scheme, until a node having a lowest recognition error is identified, said hierarchical object classification scheme defined by providing a candidate class for the detected object with an increasing degree of specificity as a hierarchy of classes is traversed from a root node to a leaf node; and output a class label to identify said candidate class to which the detected object corresponds to and a probability value to indicate a probability with which the detected object belongs to said candidate class.
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21. An article of manufacture for classifying an object in image data, comprising:
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a computer-readable medium having computer-readable code means embodied thereon, said computer-readable program code means comprising; detecting an object in said image data; classifying said object using a hierarchical object classification scheme defined by providing a candidate class for the detected object with an increasing degree of specificity as a hierarchy of classes is traversed from a root node to a leaf node; and outputting a class label to identify said candidate class to which the detected object corresponds to and a probability value to indicate a probability with which the detected object belongs to said candidate class.
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22. An article of manufacture for classifying an object in image data, comprising:
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a computer-readable medium having computer-readable code means embodied thereon, said computer-readable program code means comprising; detecting an object in said image data; evaluating a recognition error for said object for one or more nodes in a hierarchical object classification scheme, until a node having a lowest recognition error is identified, said hierarchical object classification scheme defined by providing a candidate class for the detected object with an increasing degree of specificity as a hierarchy of classes is traversed from a root node to a leaf node; and outputting a class label to identify said candidate class to which the detected object corresponds to and a probability value to indicate a probability with which the detected object belongs to said candidate class.
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Specification