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Investigating the effects of scale in MRF texture classification Scott Blunsden1, Louis Atallah2
 

Summary: Investigating the effects of scale in MRF texture classification
Scott Blunsden1, Louis Atallah2
1 School of Informatics, University of Edinburgh, s.j.blunsden@sms.ed.ac.uk
2 The British University in Dubai/ University of Edinburgh, PO Box 502216, Dubai, UAE, latallah@inf.ed.ac.uk
Keywords:Texture, Classification, Scale, MRF
Abstract
This work sheds the light on an important problem that
faces real-world texture classification. That of incorporating
textural information present at several scales and the
robustness of classifiers to viewing distance and zooming.
A Markov Random field framework is considered and the
Varma-Zisserman classifier [16] (VZ classifier) is used as a
starting point due to its high rates of classification on some
difficult datasets (the CUReT dataset for example). A region
selector (the scale-saliency algorithm by Kadir and Brady
[5]) is incorporated in the VZ classifier to select `salient'
or significant areas in an image and use them for texture
classification. The performance of this method on several
datasets is discussed and analysed (namely the CUReT and
the Brodatz datasets). The VZ classifier is then updated to

  

Source: Atallah, Louis - Department of Computing, Imperial College, London

 

Collections: Computer Technologies and Information Sciences