Regular Articles

Texture image classification using modular radial basis function neural networks

[+] Author Affiliations
Chuan-Yu Chang

National Yunlin University of Science and Technology, Department of Computer Science and Information Engineering, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan

Hung-Jen Wang

National Yunlin University of Science and Technology, Graduate School of Engineering Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan

Shih-Yu Fu

National Yunlin University of Science and Technology, Department of Computer Science and Information Engineering, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan

J. Electron. Imaging. 19(1), 013013 (March 15, 2010). doi:10.1117/1.3358377
History: Received September 24, 2009; Revised January 10, 2010; Accepted January 27, 2010; Published March 15, 2010; Online March 15, 2010
Text Size: A A A

Image classification has become an important topic in multimedia processing. Recently, neural network-based methods have been proposed to solve the classification problem. Among them, the radial basis function neural network (RBFNN) is the most popular architecture, because it has good learning and approximation capabilities. However, traditional RBFNNs are sensitive to center initialization. To obtain appropriate centers, it needs to find significant features for further RBF clustering. In addition, the training procedure of a traditional RBFNN is time consuming. Therefore, in this work, a combination of a self-organizing map (SOM) and learning vector quantization (LVQ) neural networks is proposed to select more appropriate centers for an RBFNN, and a modular RBF neural network (MRBFNN) is proposed to improve the classification rate and to speed up the training time. Experimental results show that the proposed MRBFNN has better performance than those of the traditional RBFNN, the discrete wavelength transform (DWT)-based method, the tree structured wavelet (TWS), the discrete wavelet frame (DWF), the rotated wavelet filter (RWF), and the wavelet neural network based on adaptive norm entropy (WNN-ANE) methods.

Figures in this Article
© 2010 SPIE and IS&T

Citation

Chuan-Yu Chang ; Hung-Jen Wang and Shih-Yu Fu
"Texture image classification using modular radial basis function neural networks", J. Electron. Imaging. 19(1), 013013 (March 15, 2010). ; http://dx.doi.org/10.1117/1.3358377


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.