Image classification using a stochastic model that reflects the internal structure of mixels

  • Asanobu Kitamoto
  • Mikio Takagi
Statistical Classification Techniques
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

Abstract

This paper proposes new ideas for the classification of images with the presence of mixels, or mixed pixels. Based on the internal structure of mixels, we first propose a stochastic model called area proportion density, and we demonstrate that Beta distribution is an appropriate model for this density. Next, based on the linear model of a mixel, we derive another stochastic model called mixel density. This model is then incorporated into the mixture density model of the image histogram, and we show the peculiar flat shape of this model works particularly effective for image histograms with long tail. Finally we present experiments on satellite imagery, and the goodness-of-fit of the proposed model is evaluated from the viewpoint of information criterion.

Keywords

Akaike Information Criterion Image Classification Beta Distribution Classification Class Mixture Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Asanobu Kitamoto
    • 1
  • Mikio Takagi
    • 2
  1. 1.R&D DepartmentNational Center for Science Information Systems (NACSIS)TokyoJapan
  2. 2.Department of Applied ElectronicsScience University of TokyoChibaJapan

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