Texture classification with a dictionary of basic image features

Many successful recent approaches to texture classification model texture images as distributions over a set of discrete features, or textons, which correspond to a partitioning of the space of responses to local descriptors such as filter banks or image patches. This partitioning is learned by unsupervised clustering of descriptor responses taken from the dataset to be analysed. Here, we explore a quantization of filter responses into a dictionary of discrete features which is based on geometrical, rather than statistical, considerations, resulting in a simple texture description based on a dictionary of dasiavisual wordspsila which is independent of the images to be described. A multi-scale classification scheme built on this dictionary is evaluated. The results presented are, to the best of our knowledge, state-of-the-art for the UIUCTex and KTH-TIPS datasets, and close to the state-of-the-art for CUReT, despite using a less sophisticated classifier.