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Error Diffused Halftohe Classification Using Stochastic Geometrical Features

Digital halftoning is a strategy to convert the color or gray scale image into a printable format. Among several halftoning techniques, error diffusion is one of the conventional and widely adopted method in many printing devices. In further, halftone classification is very important to obtain the perfect reconstruction of binary printed images. The paper attempts to solve this issue by exploiting the intrinsic similarity between the stochastic geometry and halftoning. Feature vectors are constructed using the point process statistic parameters such as directional distribution function and radially averaged power spectral density. Extreme learning machine model is developed and eight varieties of error diffusion halftone images are considered for classification. From the results, it has been validated that the proposed scheme yields better accuracy of 97.5% and it is faster than the existing approaches.