Improving texture extraction and classification using smoothed morphological operators

To improve texture recognition, this paper proposes the application of a morphological transformation in the original dataset. The smoothed shock filtering method uses smoothed dilations and erosions operations to produce enhanced images respecting its edges. As the preprocessing method is applied iteratively, it produces a scale space representation, which allows us to analyze combinations of them to check the ones that give the best results for texture recognition. To describe transformed and original images, six different texture analysis methods will be used. Results of the proposed method are compared with the original approach (without any preprocessing method) and the use of two classic diffusion algorithms. Achievements with our smoothed shock filter show a classification rate of 99.54% for Vistex classification using CLBP and KNN (k=1) as the classifier.