CFA local binary patterns for fast illuminant-invariant color texture classification
This paper focuses on the classification of color textures acquired by single-sensor color cameras under various illuminants. Local binary patterns (LBPs) are robust texture descriptors suited to such conditions. This property is still improved when LBPs are computed from the level ranks. Our main contribution is to avoid the demosaicing step that is classically performed in single-sensor color cameras to estimate color images from raw data. We instead compute rank-based LBPs from the color filter array image, in which each pixel is associated to a single color component. Experimental results achieved on a benchmark color texture database show the effectiveness of the proposed approach for texture classification, and a complexity study highlights its computational efficiency.
local binary patterns color texture classification illuminant invariance bayer color filter array