Unsupervised clustering on dynamic databases
Pattern Recognition Letters
A fast VQ codebook generation algorithm via pattern reduction
Pattern Recognition Letters
Accelerating VQ-based codeword search on the basis of partial search strategy
Computer Standards & Interfaces
Generalizing the k-Windows clustering algorithm in metric spaces
Mathematical and Computer Modelling: An International Journal
An Improved Tree-Structured Codebook Search Algorithm for Grayscale Image Compression
Fundamenta Informaticae
Data Compressor for VQ Index Tables
Fundamenta Informaticae
PREACO: A fast ant colony optimization for codebook generation
Applied Soft Computing
Hi-index | 35.68 |
Fast search algorithms are proposed and studied for vector quantization encoding using the K-dimensional (K-d) tree structure. Here, the emphasis is on the optimal design of the K -d tree for efficient nearest neighbor search in multidimensional space under a bucket-Voronoi intersection search framework. Efficient optimization criteria and procedures are proposed for designing the K-d tree, for the case when the test data distribution is available (as in vector quantization application in the form of training data) as well as for the case when the test data distribution is not available and only the Voronoi intersection information is to be used. The criteria and bucket-Voronoi intersection search procedure are studied in the context of vector quantization encoding of speech waveform. They are empirically observed to achieve constant search complexity for O(log N) tree depths and are found to be more efficient in reducing the search complexity. A geometric interpretation is given for the maximum product criterion, explaining reasons for its inefficiency with respect to the optimization criteria