Robust features for textures in additive noise

Abstract

The paper describes a method for texture classification in noise by using third-order cumulants as discriminating features. The problem is formulated as a test on K hypotheses and solved by a Maximum Likelihood (ML) criterium applied in the third-order cumulant domain. Since in the case of image processing complete third-order cumulant computation is not feasible, we reduced the estimation to a limited number of cumulant slices and lags. This reduction makes the classification algorithm suboptimal. Thus, a criterion for the choice of cumulant samples to be computed is introduced in order to guarantee the selection of those lags which better identify the different textures in the training phase of the classifier.

Experimental tests are carried out to evaluate third-order cumulant performances on noisy textures and the importance of lags selection.

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