2011 Halftone image classification using LMS algorithm and naive Bayes

Yun-Fu Liu, Jing-Ming Guo, and Jiann-Der Lee, "Halftone image classification using LMS algorithm and naive Bayes," IEEE Trans. Image Processing, vol. 20, no. 10, October 2011.

Abstract:

• Former research on inverse halftoning most focus on developing a general-purpose method for all types of halftone patterns, such as error diffusion, ordered dithering, etc while fail to consider the natural discrepancies among various halftoning methods. To achieve optimal image quality for each halftoning method, the classification of halftone images is highly demanded. This study employed the Least-Mean-Square (LMS) filter for improving the robustness of the extracted features and employed the naive Bayes classifier to verify all the extracted features for classification. Nine of the most well-known halftoning methods were involved for testing. The experimental results demonstrated that the classification performance can achieve a 100% accuracy rate, and the number of distinguishable halftoning methods is more than that of a former method established by Chang-Yu.

Online resources:

• Complete article [pdf file]