Halftone Image Classification Using LMS Algorithm and Naive Bayes

被引：30

作者：

Liu, Yun-Fu ^{[1
]}

Guo, Jing-Ming ^{[1
]}

Lee, Jiann-Der ^{[2
]}

机构：

[1] Natl Taiwan Univ Sci & Technol, Dept Elect Engn, Taipei 106, Taiwan

[2] Chang Gung Univ, Dept Elect Engn, Tao Yuan 33302, Taiwan

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 2011年 / 20卷 / 10期

关键词：

Bayes theorem; halftone image classification; halftoning; image analysis; inverse halftoning; DOT DIFFUSION; INVERSE; RESTORATION;

D O I：

10.1109/TIP.2011.2136354

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：2837 / 2847

页数：11