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CFA local binary patterns for fast illuminant-invariant color texture classification

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Abstract

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.

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Notes

  1. This definition ignores border effects for readability sake. Only pixels whose neighborhood \(\mathcal{N}_d\) is fully enclosed in the image are actually taken into account to compute the LBP histogram.

  2. Histogram normalization (see Eq. 4) is crucial here because \(|\tilde{I}^{G,q}_{\rm CFA}| = \frac{1}{2}|\tilde{I}^{G,q}|\) and \(|\tilde{I}^{k,q}_{\rm CFA}| = \frac{1}{4}|\tilde{I}^{k,q}|\) (k = R, B).

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  1. Laboratoire LAGIS, UMR CNRS 8219, Université Lille 1 - Sciences et Technologies Cité Scientifique, Bâtiment P2, 59655, Villeneuve d’Ascq, France 

    Olivier Losson & Ludovic Macaire

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  1. Olivier Losson
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  2. Ludovic Macaire
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Correspondence to Olivier Losson.

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Losson, O., Macaire, L. CFA local binary patterns for fast illuminant-invariant color texture classification. J Real-Time Image Proc 10, 387–401 (2015). https://doi.org/10.1007/s11554-012-0302-5

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