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Advances in Multimedia
Volume 2016, Article ID 4985313, 10 pages
Research Article

Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

1College of Computer and Communication, Hunan University of Technology, Hunan 412007, China
2Intelligent Information Perception and Processing Technology, Hunan Province Key Laboratory, Hunan 412007, China
3Department of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China

Received 21 January 2016; Revised 22 March 2016; Accepted 18 April 2016

Academic Editor: Stefanos Kollias

Copyright © 2016 Zhigao Zeng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a novel algorithm to solve the challenging problem of classifying error-diffused halftone images. We firstly design the class feature matrices, after extracting the image patches according to their statistics characteristics, to classify the error-diffused halftone images. Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. As demonstrated by the experimental results, our method is fast and can achieve a high classification accuracy rate with an added benefit of robustness in tackling noise.