Boosting k-NN for Categorization of Natural Scenes

摘要

The k-nearest neighbors (k-NN) classification rule has proven extremely successful in countless many computer vision applications. For example, image categorization often relies on uniform voting among the nearest prototypes in the space of descriptors. In spite of its good generalization properties and its natural extension to multi-class problems, the classic k-NN rule suffers from high variance when dealing with sparse prototype datasets in high dimensions. A few techniques have been proposed in order to improve k-NN classification, which rely on either deforming the nearest neighborhood relationship by learning a distance function or modifying the input space by means of subspace selection. From the computational standpoint, many methods have been proposed for speeding up nearest neighbor retrieval, both for multidimensional vector spaces and nonvector spaces induced by computationally expensive distance measures.