Multi-scale Gray Level and Local Difference for texture classification
Norhene Gargouri Ben Ayed, Malek Gargouri Larousi, Alima Damak Masmoudi, Dorra Sellami Masmoudi, Riadh Abid · 2014
In this paper, we present a novel extension of the Gray Level and Local Difference (GLLD) method and it is named as Multi-scale GLLD for texture classification. In the GLLD, a local region is described by its central pixel and the local difference sign-magnitude. The central pixels representing the image gray level are transformed into a binary code by global thresholding. The local difference sign-magnitude is based on the image decomposition into two complementary components: the signs and the magnitudes. By combining SGLLD, MGLLD, and CGLLD features, momentous improvement can be made in terms of texture classification. As an extension of the GLLD, we proposed to apply the multi-scale scheme and we obtained better results. The classification rate of the corresponding approach reached 96%. A comparative study with previous approaches confirms that the proposed approach presents the best performances.