Spatial Morphological Covariance Applied to Texture Classification

Morphological covariance, one of the most frequently employed texture analysis tools offered by mathematical morphology, makes use of the sum of pixel values, i.e. “volume” of its input. In this paper, we investigate the potential of alternative measures to volume, and extend the work of Wilkinson (ICPR’02) in order to obtain a new covariance operator, more sensitive to spatial details, namely the spatial covariance. The classification experiments are conducted on the publicly available Outex 14 texture database, where the proposed operator leads not only to higher classification scores than standard covariance, but also to the best results reported so far for this database when combined with an adequate illumination invariance model.