Multidimensional close neighbor search

Image analysis – Image compression or coding – Quantization

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707 5, 382240, 382232, G06F 1730

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active

059110112

ABSTRACT:
A method for a multidimensional search for a close neighbor of an input vector, amongst a first set of reference vectors, comprises the prior determination of a first set of hyperplanes in the space containing the reference vectors, then selection of a first hyperplane from the first set, formation of a second set of reference vectors, by eliminating reference vectors which are on the other side of the first hyperplane selected, compared with the input vector, formation of a second set of hyperplanes, by eliminating the said first hyperplane, reiteration, a predetermined number of times, of the selection and formation operations, taking, as the first set of reference vectors and as the first set of hyperplanes, respectively, the second sets formed previously, and searching for the closest neighbor of the input vector in the second set of reference vectors.

REFERENCES:
patent: 5414527 (1995-05-01), Koshi et al.
patent: 5468069 (1995-11-01), Prasanna
patent: 5553163 (1996-09-01), Nivelle
Ramasubramanian, et al. "Fast K-Dimensional Tree Algorithms for Nearest Neighbor Search with Application to Vector Quantization Encoding", 8084 IEEE Transaction on Signal Processing 40(1992) Mar. No. 3, New York, US.
Cheng, et al. "A Fast Codebook Search Algorithm for Nearest-Neighbor Pattern Matching", 1986 IEEE, pp. 265-268.
Madisetti, et al. "A Radius-Bucketing Approach to Fast Vector Quantization Encoding", 1989 IEEE, pp. 1767-1770.
M. Kamel et al., "Fast Nearest Neighbor Search for Vector Quantization of Image Data," 1192 11th Int'l Conference on Pattern Recognition, vol. III: Conference C, IEEE, pp. 623-626, 1992.

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