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

Abstract EN

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.