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- W2023173904 abstract "The problem of finding the stored template that is closest to a given input pattern is a typical problem in vector quantization (VQ) encoding and nearest neighbor (NN) pattern classification. This paper presents a new Triangle Inequality Nearest Neighbor Search (TINNS) algorithm that significantly reduces the number of distance calculations. This algorithm is appropriate in applications for which the computational cost of making a distance calculation is relatively expensive. Automatic Target Recognition (ATR) is one such application. This new algorithm achieves improved performance by guiding the order in which templates are tested, and using inequality constraints to prune the search space. We compare TINNS with another competing approach, as well as exhaustive search, and show that there is an appropriate application domain for each algorithm. Results are given for three applications, VQ for image compression, NN search over random templates, and target recognition in synthetic aperture radar image data." @default.
- W2023173904 created "2016-06-24" @default.
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- W2023173904 date "2001-08-27" @default.
- W2023173904 modified "2023-09-23" @default.
- W2023173904 title "<title>Search algorithms for vector quantization and nearest-neighbor classification</title>" @default.
- W2023173904 doi "https://doi.org/10.1117/12.438220" @default.
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