Nearest Neighbour Strategies for Image Understanding

Nearest Neighbour algorithms for pattern recognition have been widely studied. It is now well-established that they offer a quick and reliable method of data classification. In this paper we further develop 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 (kNN model). We also propose aNN model 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 when compared with neural networks. 1 © British Crown Copyright 1999/DERA Published with the permission of the controller of Britannic Majesty's Stationary Office; S. Singh, J.F. Haddon and M. Markou. Nearest Neighbour Strategies for Image Understanding, Proc. Workshop on Advanced Concepts for Intelligent Vision, Systems (ACIVS'99), Baden-Baden, (2-7 August, 1999).