Texture Classification of Graylevel Images by Multiscale Cross-Co-Occurrence Matrices

  • Local gray level dependencies of natural images can be modelled by means of co-occurrence matrices containing joint probabilities of gray-level pairs. Texture, however, is a resolution-dependent phenomenon and hence, classification depends on the chosen scale. Since there is no optimal scale for all textures we employ a multiscale approach that acquires textural features at several scales. Thus linear and nonlinear scale-spaces are analyzed by multiscale co-occurrence matrices that describe the statistical behavior of a texture in scale-space. Classification is then performed on the basis of texture features taken from the individual scale with the highest discriminatory power. By considering cross-scale occurrences of gray level pairs, the impact of filters on the feature is described and used for classification of natural textures. This novel method was found to improve classification rates of the common co-occurrence matrix approach on standard textures significantly.

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Metadaten
Author:V. Metzler, T. Aach, Christoph PalmORCiDGND, Thomas M. Lehmann
DOI:https://doi.org/10.1109/ICPR.2000.906133
Parent Title (English):Proceedings 15th International Conference on Pattern Recognition (ICPR-2000)
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2000
Release Date:2020/05/18
First Page:549
Last Page:552
Institutes:Fakult├Ąt Informatik und Mathematik
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme
Publication:Externe Publikationen