Local Statistical Operators for Texture Classification

We investigate texture classification from single images obtained under unknown viewpoint and illumination. It is demonstrated that materials can be classified using the joint distribution of intensity values over extremely compact neighbourhoods (starting from as small as 3 × 3 pixels square), and that this outperforms classification using filter banks with large support. It is also shown that the performance of filter banks is inferior to that of image patches with equivalent neighbourhoods.

We develop novel texton based representations which are suited to modelling this joint neighbourhood distribution for MRFs. The representations are learnt from training images, and then used to classify novel images (with unknown viewpoint and lighting) into texture classes. Three such representations are proposed, and their performance is assessed and compared to that of filter banks.

The power of the method is demonstrated by classifying 2806 images of all 61 materials present in the Columbia-Utrecht database. The classification performance surpasses that of recent state of the art filter bank based classifiers such as Leung and Malik (IJCV 01), Cula and Dana (IJCV 04), and Varma and Zisserman (IJCV 05). We also benchmark performance by classifying all the textures present in the Microsoft Textile database as well as the San Francisco outdoor dataset.

We conclude with discussions on why features based on compact neighbourhoods can correctly discriminate between textures with large global structure and why the performance of filter banks is not superior to the source image patches from which they were derived.