Feature extraction using dual-tree complex wavelet transform and gray level co-occurrence matrix

被引:63
作者
Yang, Peng [1 ]
Yang, Guowei [1 ]
机构
[1] Nanchang Hangkong Univ, Sch Informat Engn, Nanchang, Jiangxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Feature extraction; Texture classification; Dual-tree complex wavelet transform; Gray level co-occurrence matrix; TEXTURE CLASSIFICATION; ROTATION; ENTROPY;
D O I
10.1016/j.neucom.2016.02.061
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper introduces a new feature extraction method for texture classification application. In the proposed method, dual-tree complex wavelet transform is first performed on the original image to obtain sub-images at six directions. After that gray level co-occurrence matrix of each sub-image is calculated and the corresponding statistical values are used to construct the final feature vector. The experimental results demonstrate that our proposed method has the property of robustness, and can achieve higher texture classification accuracy rate than the conventional methods. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:212 / 220
页数:9
相关论文
共 32 条
[31]   Pathological brain detection based on wavelet entropy and Hu moment invariants [J].
Zhang, Yudong ;
Wang, Shuihua ;
Sun, Ping ;
Phillips, Preetha .
BIO-MEDICAL MATERIALS AND ENGINEERING, 2015, 26 :S1283-S1290
[32]   Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM) [J].
Zhang, Yudong ;
Dong, Zhengchao ;
Wang, Shuihua ;
Ji, Genlin ;
Yang, Jiquan .
ENTROPY, 2015, 17 (04) :1795-1813