Textures: an approach for new abstract description language

Texture analysis plays an important role for automatic image segmentation and object recognition. Objects and regions in an image can be distinguished by their texture, where the distinction arises from the different physical surface properties of the objects represented. To a human observer the different textures in an image are usually very apparent, but the verbal description of the visual properties of these patterns is a difficult and ambiguous task. In computer vision it has turned out in theoretical and experimental comparisons of different methods that the co-occurrence matrix is suitable for texture analysis. Therefore, in this approach the co-occurrence matrix is used as a mathematical model for natural textures. We propose a promising improvement for texture classification and description in the context of natural textures. After developing a new abstract language for describing visual properties of natural textures, we establish a relation between these visual properties used by a human observer, and statistical textural features computed out of the digital image data. Our experiments indicate that some statistical features are more significant for classifying natural textures than others. Finally we apply our new approach on landscape scenes: we show how the new language is used for defining texture classes.