Local Directional Texture Pattern image descriptor

• We created a micro-pattern that models the information of the principal directions.
• We discriminate and select the prominent information in each neighborhood.
• We model texture and structure simultaneously.
• We created a code that is versatile and works on large and small textures.

Deriving an effective image representation is a critical step for a successful automatic image recognition application. In this paper, we propose a new feature descriptor named Local Directional Texture Pattern (LDTP) that is versatile, as it allows us to distinguish person’s expressions, and different landscapes scenes. In detail, we compute the LDTP feature, at each pixel, by extracting the principal directions of the local neighborhood, and coding the intensity differences on these directions. Consequently, we represent each image as a distribution of LDTP codes. The mixture of structural and contrast information makes our descriptor robust against illumination changes and noise. We also use Principal Component Analysis to reduce the dimension of the multilevel feature set, and test the results on this new descriptor as well.