A general approach to d-dimensional geometric queries
STOC '85 Proceedings of the seventeenth annual ACM symposium on Theory of computing
A randomized algorithm for closest-point queries
SIAM Journal on Computing
Point location in arrangements of hyperplanes
Information and Computation
Prototype selection for the nearest neighbour rule through proximity graphs
Pattern Recognition Letters
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Approximate nearest neighbor queries in fixed dimensions
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Object recognition using summed features classifier
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Networks of transform-based evolvable features for object recognition
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The nearest neighbor (NN) classifier is well suited for generic object recognition. However, it requires storing the complete training data, and classification time is linear in the amount of data. There are several approaches to improve runtime and/or memory requirements of nearest neighbor methods: Thinning methods select and store only part of the training data for the classifier. Efficient query structures reduce query times. In this paper, we present an experimental comparison and analysis of such methods using the ETH-80 database. We evaluate the following algorithms. Thinning: condensed nearest neighbor, reduced nearest neighbor, Baram's algorithm, the Baram-RNN hybrid algorithm, Gabriel and GSASH thinning. Query structures: kd-tree and approximate nearest neighbor. For the first four thinning algorithms, we also present an extension to k-NN which allows tuning the trade-off between data reduction and classifier degradation. The experiments show that most of the above methods are well suited for generic object recognition.