Fast k-nearest-neighbor search based on projection and triangular inequality
Pattern Recognition
Full-Searching-Equivalent Vector Quantization Using Two-Bounds Triangle Inequality
Fundamenta Informaticae
A fast VQ codebook generation algorithm using codeword displacement
Pattern Recognition
A novel approach for fast codebook re-quantization
Pattern Recognition
Improvement of the fast exact pairwise-nearest-neighbor algorithm
Pattern Recognition
Fast Searching Algorithm for Vector Quantization Based on Subvector Technique
IEICE - Transactions on Information and Systems
A fast VQ codebook generation algorithm via pattern reduction
Pattern Recognition Letters
A novel encoding algorithm for vector quantization using transformed codebook
Pattern Recognition
Image and Vision Computing
Fast global k-means clustering using cluster membership and inequality
Pattern Recognition
Fast agglomerative clustering using information of k-nearest neighbors
Pattern Recognition
Fast requantization using self organizing feature map with orthogonal polynomials transform
Proceedings of the 2011 International Conference on Communication, Computing & Security
An agglomerative clustering algorithm using a dynamic k-nearest-neighbor list
Information Sciences: an International Journal
Full-Searching-Equivalent Vector Quantization Using Two-Bounds Triangle Inequality
Fundamenta Informaticae
An edge preserving requantization model for color image coding with orthogonal polynomials
Digital Signal Processing
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In this paper, a new and fast-searching algorithm for vector quantization is presented. Two inequalities, one used for terminating the searching process and the other used to delete impossible codewords, are presented to reduce the distortion computations. Our algorithm makes use of a vector's features (mean value, edge strength, and texture strength) to reject many unlikely codewords that cannot be rejected by other available approaches. Experimental results show that our algorithm is superior to other algorithms in terms of computing time and the number of distortion calculations. Compared with available approaches, our method can reduce the computing time and the number of distortion computations significantly. Compared with the best method of reducing distortion computation, our algorithm can further reduce the number of distortion calculations by 29% to 58.4%. Compared with the best encoding algorithm for vector quantization, our approach also further reduces the computing time by 8% to 47.7%.