Rotation-invariant texture classification using feature distributions

被引：338

作者：

Pietikäinen, M ^{[1
]}

Ojala, T ^{[1
]}

Xu, Z ^{[1
]}

机构：

[1] Oulu Univ, Infotech Oulu, Dept Elect Engn, Machine Vis & Media Proc Grp, FIN-90401 Oulu, Finland

来源：

PATTERN RECOGNITION | 2000年 / 33卷 / 01期

基金：

芬兰科学院;

关键词：

texture analysis; classification; feature distribution; rotation invariant; performance evaluation;

D O I：

10.1016/S0031-3203(99)00032-1

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 random field (CSAR) model approach. A difficult classification problem of 15 different Brodatz textures and seven rotation angles is used in experiments. 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 classification problem and the features used in this study. (C) 1999 Published by Elsevier Science Ltd. All rights reserved.

引用

页码：43 / 52

页数：10

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