Halftone image classification using LMS algorithm and naive bayes

Yun Fu Liu*, Jing Ming Guo, Jiann Der Lee

*此作品的通信作者

研究成果: 期刊稿件文章同行評審

45 引文 斯高帕斯(Scopus)

摘要

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 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 and Yu.

原文英語
文章編號5741851
頁(從 - 到)2837-2847
頁數11
期刊IEEE Transactions on Image Processing
20
發行號10
DOIs
出版狀態已出版 - 10 2011

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