Texture Classification: Are Filter Banks Necessary?

We question the role that large scale filter banks have traditionally played in texture classification. It is demonstrated that textures can be classified using the joint distribution of intensity values over extremely compact neighbourhoods (starting from as small as 33 pixels square), and that this outperforms classification using filter banks with large support.

We develop a novel texton based representation which is suited to modelling this joint neighbourhood distribution for MRFs. The representation is learnt from training images, and then used to classify novel images (with unknown viewpoint and lighting) into texture classes.

The power of the method is demonstrated by classifying over 2800 images of all 61 textures present in the Columbia-Utrecht database. The classification performance surpasses that of recent state-of-the-art filter bank based classifiers such as Leung & Malik [IJCV 01], Cula &Dana [CVPR 01], and Varma&Zisserman [ECCV 02].