Nearest-neighbour classifiers in natural scene analysis

It is now well-established that k nearest-neighbour classifiers offer a quick and reliable method of data classification. In this paper we extend the basic definition of the standard k nearest-neighbour algorithm to include the ability to resolve conflicts when the highest number of nearest neighbours are found for more than one training class (model-l). We also propose model-2 of nearest-neighbour algorithm that is based on finding the nearest average distance rather than nearest maximum number of neighbours. These new models are explored using image understanding data. The models are evaluated on pattern recognition accuracy for correctly recognising image texture data of five natural classes: grass, trees, sky, river reflecting sky and river reflecting trees. On noise contaminated test data, the new nearest neighbour models show very promising results for further studies. We evaluate their performance with increasing values of neighbours (k) and discuss their future in scene analysis research. Crown Copyright (C) 2001 Published by Elsevier Science Ltd. on behalf of Pattern Recognition Society. All rights reserved.