Sparse Representation and Dictionary Learning as Feature Extraction in Vessel Imagery

Abstract

This report describes experiments designed to evaluate the usefulness of a specific algorithm for classifying images of commercial ships by class. This algorithm uses a technique known as sparse coding to represent images for classification. The sparse coding algorithm is compared with another algorithm evaluated in previous publications. The sparse coding algorithm is shown to perform approximately as well as the algorithm it is compared with and does not appear to offer any improvement. Additional research is required to identify algorithms best suited for the ship classification task.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2014
Accession Number
ADA613963

Entities

People

  • Ana Ascencio
  • Katie Rainey

Organizations

  • Naval Information Warfare Systems Command

Tags

Communities of Interest

  • C4I
  • Ground and Sea Platforms
  • Space

DTIC Thesaurus Topics

  • Classification
  • Compressed Sensing
  • Computer Vision
  • Data Sets
  • Department Of Defense
  • Digital Images
  • Feature Extraction
  • Governments
  • Image Processing
  • Image Recognition
  • Machine Learning
  • Pattern Recognition
  • Recognition
  • Satellite Imaging
  • Supervised Machine Learning
  • Target Recognition
  • United States Government

Fields of Study

  • Computer science

Readers

  • Naval Architecture and Marine Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML