Halftone image classification using LMS algorithm and naive bayes

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

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

44 Scopus citations


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.

Original languageEnglish
Article number5741851
Pages (from-to)2837-2847
Number of pages11
JournalIEEE Transactions on Image Processing
Issue number10
StatePublished - 10 2011


  • Bayes theorem
  • halftone image classification
  • halftoning
  • image analysis
  • inverse halftoning


Dive into the research topics of 'Halftone image classification using LMS algorithm and naive bayes'. Together they form a unique fingerprint.

Cite this