Object Classification from a Single Example Utilizing Class Relevance Metrics

We describe a framework for learning an object classifier from a single example. This goal is achieved by emphasizing the relevant dimensions for classification using available examples of related classes. Learning to accurately classify objects from a single training example is often unfeasible due to overfitting effects. However, if the instance representation provides that the distance between each two instances of the same class is smaller than the distance between any two instances from different classes, then a nearest neighbor classifier could achieve perfect performance with a single training example. We therefore suggest a two stage strategy. First, learn a metric over the instances that achieves the distance criterion mentioned above, from available examples of other related classes. Then, using the single examples, define a nearest neighbor classifier where distance is evaluated by the learned class relevance metric. Finding a metric that emphasizes the relevant dimensions ...

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Michael Fink 0002
• Keywords: Nearest Neighbor Classifier, NIPS 2004, NIPS 2007, Single Example, Single Training Example