Rotation-invariant texture classification using feature distributions. Pietikäinen, M.; Ojala, T.; and Xu, Z. 2000.
abstract   bibtex   
A distribution-based classification approach and a set of recently developed texture measures are applied to rotation-invariant texture classification. The performance is compared to that obtained with the well-known circular-symmetric autoregressive random field (CSAR) model approach. A difficult classification problem of 15 different Brodatz textures and seven rotation angles is used in e x periments. The results show much better performance for our approach than for the CSAR features. A detailed analysis of the confusion matrices and the rotation angles of misclassified samples produces several interesting observations about the classificat ion problem and the features used in this study.
@article{
 title = {Rotation-invariant texture classification using feature distributions.},
 type = {article},
 year = {2000},
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 notes = {Pattern Recognition 33:43 - 52.},
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 abstract = {A distribution-based classification approach and a set of recently developed texture measures are applied to rotation-invariant texture classification. The performance is compared to that obtained with the well-known circular-symmetric autoregressive random field (CSAR) model approach. A difficult classification problem of 15 different Brodatz textures and seven rotation angles is used in e x periments. The results show much better performance for our approach than for the CSAR features. A detailed analysis of the confusion matrices and the rotation angles of misclassified samples produces several interesting observations about the classificat ion problem and the features used in this study.},
 bibtype = {article},
 author = {Pietikäinen, M and Ojala, T and Xu, Z}
}
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