Illumination-Invariant Texture Classification Using Single Training Images

The appearance of a surface texture is highly dependent on illumination. This is why current surface texture classification methods require multiple training images captured under a variety of illumination conditions for each class. We show that a single training image per class can be sufficient if the surfaces are of uniform albedo and smooth and shallow relief, and the illumination is sufficiently far from the texture macro-normal. The performance of our approach is demonstrated on classification of 20 textures in the PhoTex database. For test images which are most different from the training images (different instances of the same texture observed, non-equal illumination slants), the success rate achieved is in the range of 60–80%. When the test images differ from the training ones only in illumination tilt, the success rate achieved is well above 95%.