Learning a discriminative classifier using shape context distances
DOI: 10.1109/CVPR.2003.1211360
关键词: Classifier (UML) 、 Contextual image classification 、 Cognitive neuroscience of visual object recognition 、 Pattern recognition 、 Boosting (machine learning) 、 Mathematics 、 Artificial intelligence 、 Shape context 、 Margin classifier 、 Feature vector 、 Machine learning 、 Discriminative model
摘要: For the purpose of object recognition, we learn one discriminative classifier based on prototype, using shape context distances as feature vector. From multiple prototypes, outputs classifiers are combined method called "error correcting output codes". The overall is tested a benchmark dataset and shown to outperform existing methods with far fewer prototypes.
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