Use of Eigenvector-Generated Scatter Plots in Clustering Image Data

Abstract

Over the last few years we have been analyzing state-of-the-art spectral /temporal data of many events. Our goal was to develop specific techniques to classify and identify events based on these measurements. While the techniques evolved from one data type, we focus in this paper on the technique itself and its potential efficacy when applied to other data types. We use a Singular Value Decomposition (SVD) technique to cluster like events by forming a scatter plot from the first two eigenvectors. An evaluation of this approach using real data as well as simulations is given. A novel technique is introduced to assess cluster stability in the absence of ground truth. Results are presented along with the effects of misalignment of data samples, compression, training sets, and classifiers. The overall methodology is quite powerful and has remarkable noise immunity.

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Document Details

Document Type
Technical Report
Publication Date
Jul 28, 2008
Accession Number
ADA483978

Entities

People

  • Charlene Caefer
  • Jerry Silverman

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Compression
  • Data Sets
  • Databases
  • Detection
  • Detectors
  • Eigenvectors
  • Gaussian Noise
  • Government Procurement
  • Machine Learning
  • Measurement
  • Simulations
  • Standards
  • Training
  • Two Dimensional

Readers

  • Computer Vision.
  • Linear Algebra
  • Neural Network Machine Learning.