Texture discrimination with multidimensional distributions of signed gray-level differences

The statistics of gray-level differences have been successfully used in a number of texture analysis studies. In this paper we propose to use signed gray-level differences and their multidimensional distributions for texture description. The present approach has important advantages compared to earl...

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Bibliographic Details
Published in:Pattern recognition 2001-03, Vol.34 (3), p.727-739
Main Authors: Ojala, Timo, Valkealahti, Kimmo, Oja, Erkki, Pietikäinen, Matti
Format: Article
Language:English
Subjects:
Brodatz texture
Classification
Local Binary Pattern
Segmentation
Texture analysis
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Summary:The statistics of gray-level differences have been successfully used in a number of texture analysis studies. In this paper we propose to use signed gray-level differences and their multidimensional distributions for texture description. The present approach has important advantages compared to earlier related approaches based on gray level cooccurrence matrices or histograms of absolute gray-level differences. Experiments with difficult texture classification and supervised texture segmentation problems show that our approach provides a very good and robust performance in comparison with the mainstream paradigms such as cooccurrence matrices, Gaussian Markov random fields, or Gabor filtering.
ISSN:0031-3203
1873-5142
DOI:10.1016/S0031-3203(00)00010-8