Classification and Compression of Multi-Resolution Vectors: A Tree Structured Vector Quantizer Approach

Abstract

Tree structured classifiers and quantizers have been used with good success for problems ranging from successive refinement coding of speech and images to classification of texture, faces and radar returns. Although these methods have worked well in practice there are few results on the theoretical side. We present several existing algorithms for tree structured clustering using multi-resolution data and develop some results on their convergence and asymptotic performance. We show that greedy growing algorithms will result in asymptotic distortion going to zero for the case of quantizers and prove termination in finite time for constraints on the rate. We derive an online algorithm for the minimization of distortion. We also show that a multiscale LVQ algorithm for the design of a tree structured classifier converges to an equilibrium point of a related ordinary dierential equation.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA438547

Entities

People

  • Sudhir Varma

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Authentication
  • Classification
  • Coding
  • Compression
  • Computational Complexity
  • Data Analysis
  • Data Transmission Systems
  • Differential Equations
  • Digital Communications
  • Ear
  • High Resolution
  • Low Resolution
  • Random Variables
  • Signal Processing
  • Stochastic Processes
  • Supervised Machine Learning

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.