Nonparametric Markov random field model analysis of the MeasTex test suite

This paper looks at the nonparametric, multiscale, Markov random field (MRF) model and its application in classifying the MeasTex test suite. The MeasTex test suite is a standard by which various texture classification algorithms can be compared. Typically, today's texture classification algori...

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Bibliographic Details
Main Authors: Paget, R., Longstaff, D.
Format: Conference Proceeding
Language:English
Subjects:
Application software
Classification algorithms
Earth
Markov random fields
Probability density function
Signal processing
Statistics
Synthetic aperture radar
Testing
Visualization
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Summary:This paper looks at the nonparametric, multiscale, Markov random field (MRF) model and its application in classifying the MeasTex test suite. The MeasTex test suite is a standard by which various texture classification algorithms can be compared. Typically, today's texture classification algorithms have been based on supervised classification, whereby all the classification classes have been predefined. We look at a new texture classification scheme, one that does not require a complete set of predefined classes. Instead our texture classification scheme is based on a significance test. A texture is classified on the basis of whether or not its statistical properties can be deemed to be from the same population of statistics as that define a training set texture. If not, texture is deemed unknown. The advantages and disadvantages of such a scheme are discussed in this paper.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2000.903696