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Clustering nodes in a self-organizing map using an adaptive resonance theory network
Clustering nodes in a self-organizing map using an adaptive resonance theory network
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机译:使用自适应共振理论网络在自组织图中对节点进行聚类
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摘要
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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