CLUSTERING NODES IN A SELF-ORGANIZING MAP USING AN ADAPTIVE RESONANCE THEORY NETWORK
First Claim
1. A computer-implemented method for discovering object type clusters for image data captured by a video camera, the method comprising:
- receiving a micro-feature vector including multiple micro-feature values, each micro-feature value based on at least one pixel-level characteristic of a foreground patch that depicts a foreground object;
processing the micro-feature vector by a self-organizing map adaptive resonance theory (SOM-ART) network to discover the object type clusters for the image data;
classifying the foreground object as depicting a first object type corresponding to a first object type cluster of the object type clusters when the micro-feature vector matches the first object type cluster; and
indicating that no match is found when the micro-feature vector does not correspond to any of the object type clusters for the image data.
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Abstract
Techniques are disclosed for discovering object type clusters using pixel-level micro-features extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to classify objects depicted in the image data based on the pixel-level micro-features. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects.
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Citations
20 Claims
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1. A computer-implemented method for discovering object type clusters for image data captured by a video camera, the method comprising:
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receiving a micro-feature vector including multiple micro-feature values, each micro-feature value based on at least one pixel-level characteristic of a foreground patch that depicts a foreground object; processing the micro-feature vector by a self-organizing map adaptive resonance theory (SOM-ART) network to discover the object type clusters for the image data; classifying the foreground object as depicting a first object type corresponding to a first object type cluster of the object type clusters when the micro-feature vector matches the first object type cluster; and indicating that no match is found when the micro-feature vector does not correspond to any of the object type clusters for the image data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer-readable storage medium containing a program which, when executed by a processor, performs an operation for discovering object type clusters for image data captured by a video camera, the operation comprising:
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receiving a micro-feature vector including multiple micro-feature values, each micro-feature value based on at least one pixel-level characteristic of a foreground patch that depicts a foreground object; processing the micro-feature vector by a self-organizing map adaptive resonance theory (SOM-ART) network to discover the object type clusters for the image data; classifying the foreground object as depicting a first object type corresponding to a first object type cluster of the object type clusters when the micro-feature vector matches the first object type cluster; and indicating that no match is found when the micro-feature vector does not correspond to any of the object type clusters for the image data. - View Dependent Claims (10, 11, 12, 13)
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14. A system, comprising:
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a video input source configured to provide image data; a processor; and a memory containing a program, which, when executed on the processor is configured to perform an operation discovering object type clusters for the image data captured by the video input source, the operation comprising; receiving a micro-feature vector including multiple micro-feature values, each micro-feature value based on at least one pixel-level characteristic of a foreground patch that depicts a foreground object; processing the micro-feature vector by a self-organizing map adaptive resonance theory (SOM-ART) network to discover the object type clusters for the image data; classifying the foreground object as depicting a first object type corresponding to a first object type cluster of the object type clusters when the micro-feature vector matches the first object type cluster; and indicating that no match is found when the micro-feature vector does not correspond to any of the object type clusters for the image data. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification