A Novel Classification Method of Halftone Image via Statistics Matrices

Existing classification methods tend not to work well on various error diffusion patterns. Thus a novel classification method for halftone image via statistics matrices is proposed. The statistics matrix descriptor of halftone image is constructed according to the characteristic of error diffusion filters. On this basis, an extraction algorithm is developed based on halftone image patches. The feature modeling is formulated as an optimization problem and then a gradient descent method is used to seek optimum class feature matrices by minimizing the total square error. A maximum likelihood method is proposed according to priori knowledge of training samples. In experiments, the performance evaluation method is provided and comparisons of performance between our method and seven similar methods are made. Then, the influence of parameters, performance under various attacks, computational time complexity and the limitations are discussed. From our experimental study, it is observed that our method has lower classification error rate when compared with other similar methods. In addition, it is robust against usual attacks.