Binary Search Vector Quantization

Adaptable Similarity Search Using Vector Quantization

Adaptable similarity queries based on quadratic form distance functions are widely popular in data mining applications, particularly for domains such as multimedia, CAD, molecular biology or medical image databases. Recently it has been recognized that quantization of feature vectors can substantially improve query processing for Euclidean distance functions, as demonstrated by the scan-based V...

Vector Quantization with the Reactive Tabu Search

Adaptive-search tree-structured residual vector quantization

Full-search vector quantization (VQ) provides optimal results only with high memory and computational cost. We describe the computational and memory requirements of treestructured VQ, residual VQ (RVQ), and tree-structured RVQ. We present multiple-rate, adaptive-search implementations of these VQ structures, and simulation results with video sequences. Tree-structured RVQ provides up to 1.5 db ...

Efficient vector quantization using an n-path binary tree search algorithm

We propose the utilization of a new n-path binary tree search algorithm for vector quantization. Our target is to reduce the complexity (time processing) of the vector quantizer maintaining the quantization distortion. The algorithm has been applied to an isolated digit recognizer by telephone based on DHMM and to a speaker dependent continuous speech system based on SCHMM, so we will also give...

Hausdorff Metric Based Vector Quantization of Binary Images

In this paper we present a vector quantization method to obtain perceptually meaningful descriptors from binary images for use in pattern recognition tasks. We introduce a distance measure and an averaging method based on the Hausdorff distance metric. Additionally we compare the proposed methods to existing Hard and Soft Centroid methods of vector quantization of binary images.