Paper Info
Title
CDF Space Covariance Matrix of Gabor Wavelet With Convolutional Neural Network for Texture Recognition.
Abstract
A novel method for texture image recognition is proposed in this paper. The aim of the proposed method is to represent texture by combining the Gabor wavelet transform and deep learning which are efficient techniques for image analysis. We developed the cumulative distribution function (cdf) space covariance model of Gabor wavelet (CSCM-GW), which can jointly model multivariate data in cdf space, in the Gabor wavelet domain to represent texture. The images having different sizes will be transformed by CSCM-GW into same size covariance matrices. Because CSCM-GW is based on the covariance matrix which belongs to Riemannian space, it has the high computational cost in the recognition phase. Therefore, we proposed a novel method of texture recognition called CSCM-GWF-CNN which uses CNN to project the fused covariance of CSCM-GW into low-dimensional vector space for reducing the computational cost and improving the recognition performance. The experiments on Brodatz (111) and KTH-TIPS2-b texture databases show that the proposed method is efficient for texture representation and outperforms most of the state-of-the-art recognition methods.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2891914
IEEE ACCESS
Keywords
Field
DocType
Texture recognition,covariance matrix,Gabor wavelet,multidimensional statistical model,convolutional neural network
Pattern recognition,Computer science,Convolutional neural network,Matrix (mathematics),Gabor wavelet,Feature extraction,Artificial intelligence,Deep learning,Covariance matrix,Wavelet transform,Distributed computing,Covariance
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Chaorong Li1475.47
Xiaoyun Guan200.34
Peihui Yang300.34
Yuanyuan Huang4275.77
Wei Huang52214.86
Huafu Chen637735.46