Classifying Images of Materials: Achieving Viewpoint and Illumination Independence

  • Manik Varma
  • Andrew Zisserman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2352)


In this paper we present a new approach to material classification under unknown viewpoint and illumination. Our texture model is based on the statistical distribution of clustered filter responses. However, unlike previous 3D texton representations, we use rotationally invariant filters and cluster in an extremely low dimensional space. Having built a texton dictionary, we present a novel method of classifying a single image without requiring any a priori knowledge about the viewing or illumination conditions under which it was photographed. We argue that using rotationally invariant filters while clustering in such a low dimensional space improves classification performance and demonstrate this claim with results on all 61 textures in the Columbia-Utrecht database. We then proceed to show how texture models can be further extended by compensating for viewpoint changes using weak isotropy.

The new clustering and classification methods are compared to those of Leung and Malik (ICCV 1999), Schmid (CVPR 2001) and Cula and Dana (CVPR 2001), which are the current state-of-the-art approaches.


Greedy Algorithm Training Image Texture Class Viewpoint Change Photometric Stereo 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Manik Varma
    • 1
  • Andrew Zisserman
    • 1
  1. 1.Robotics Research Group Department of Engineering ScienceUniversity of OxfordOxford

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