Material-Specific Adaptation of Color Invariant Features
For the modeling of materials, the mapping of image features onto a codebook of feature representatives receives extensive treatment. For reason of their generality and simplicity, filterbank outputs are commonly used as features. The MR8 filterbank of Varma and Zisserman is performing well in a recent evaluation. In this paper, we construct color invariant filter sets from the original MR8 filterbank. We evaluate several color invariant alternatives over more than 250 real-world materials recorded under a variety of imaging conditions including clutter. Our contribution is a material recognition framework that learns automatically for each material specifically the most discriminative filterbank combination and corresponding degree of color invariance. For a large set of materials each with different physical properties, we demonstrate the material-specific filterbank models to be preferred over models with fixed filterbanks.