Visual Training and Classification of Textured Scene Images

Classification of textures in scene images is very difficult due to the high variability of the data within and between images caused by effects such as non-homogeneity of the textures, changes in illumination, shadows, foreshortening and self-occlusion. For these reasons, finding proper features and representative training samples for a classifier is very problematic. Even defining the classes which can be discriminated with texture information is not so straightforward. In this paper, a visualization-based approach for training a texture classifier is presented. Powerful local binary patterns (LBP) are used as texture features and a self-organizing map (SOM) is employed for visual training and classification, providing very promising results in the classification of outdoor scene images.