PRINCIPAL FEATURES-BASED TEXTURE CLASSIFICATION WITH NEURAL NETWORKS

被引：11

作者：

SHANG, CG

BROWN, K

机构：

[1] Department of Computing and Electrical Engineering, Heriot-Watt University, Edinburgh

来源：

PATTERN RECOGNITION | 1994年 / 27卷 / 05期

关键词：

TEXTURE CLASSIFICATION; PRINCIPAL FEATURE EXTRACTION; NEURAL NETWORKS; PRINCIPAL COMPONENT TRANSFORMATION; CLASSIFICATION PERFORMANCE;

D O I：

10.1016/0031-3203(94)90046-9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A texture image classification system is presented based upon the use of two cascaded multi-layer feedforward neural networks. The first network transforms a set of high-dimensional and correlated feature images into another set of dimensionally compressed, uncorrelated principal feature images whilst reducing computation effort and minimizing the information lost. The second accomplishes the task of feature pattern classification by using only those principal features obtained by the former. Such a cascaded use of neural networks significantly simplifies the structure of the classification network and increases the efficiency of the overall classification process. These are demonstrated by the comprehensive results obtained from practical applications of the system.

引用

页码：675 / 687

页数：13

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