Performance Evaluation on Feature Modeling of Halftone Image Texture

Abstract

Three statistics methods, gray-level co-occurrence matrix, autocorrelation function and spectrum statistics, were used to extract feature vector of various halftone images for halftone image classification. The classification perfor- mances of three kinds of feature vectors were assessed by three classifiers: radial basis function neural network, least mean square and principal component analysis. Experimental results showed the autocorrelation function is better than other two methods for classification of halftone image. It indicated the best classification performance when the parameter K=64 and L=8.