The Open Automation and Control Systems Journal

2014, 6 : 250-261
Published online 2014 December 16. DOI: 10.2174/1874444301406010250
Publisher ID: TOAUTOCJ-6-250

Performance Evaluation on Feature Modeling of Halftone Image Texture

Zhiqiang Wen , Zhiyan Zhong , Degang Ye and Wenqiu Zhu
School of Computer and Communication, Hunan University of Technology, Zhuzhou, 412007, China.

ABSTRACT

Three statistics methods, gray-level co-occurrence matrix, autocorrelation function and spectrum statistics, were used to extract feature vector of various halftone images for halftone image classification. The classification performances of three kinds of feature vectors were assessed by three classifiers: radial basis function neural network, least mean square and principal component analysis. Experimental results showed the autocorrelation function is better than other two methods for classification of halftone image. It indicated the best classification performance when the parameter K=64 and L=8.

Keywords:

halftone image, inverse halftoning, modeling, statistical method, texture feature.