Rotation-invariant texture classification using feature distributions

被引：0
|
作者：
Pietikainen, M
Xu, Z
Ojala, T
机构：
来源：
SCIA '97 - PROCEEDINGS OF THE 10TH SCANDINAVIAN CONFERENCE ON IMAGE ANALYSIS, VOLS 1 AND 2 | 1997年
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D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 (CAR) model approach. A difficult classification problem with 15 different Brodatz textures and seven rotation angles is used in experiments. The results show much better performance for our approach using distributions of single features or joint pairs of features than for the CAR model method with an ordinary kNN classifier.
引用
收藏
页码：103 / 110
页数：8
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