Experimental evaluation of cumulant-based classifiers on noisy images

The paper describes a cumulant-based classifier whose discrimination criterion exploits statistical signal characteristics of higher order than the second one. The performances of the classifier were tested on texture images. In texture classification, the discrimination criterion is usually based on structural characteristics (edge density, co-occurrence matrix) or on statistical parameters (Gaussian-Markov random fields, fractal dimension) of sample textures. As an alternative to statistical approaches, in this paper a third-order cumulant-based criterion is applied and the classifier´s performances on images affected by different types of noise are assessed