  Local binary pattern (LBP) as a kind of local feature has shown its
simplicity, easy implementation and strong discriminating power in image
recognition. Although some LBP variants are specifically investigated for color
image recognition, the color information of images is not adequately considered
and the curse of dimensionality in classification is easily caused in these
methods. In this paper, a color-related local binary pattern (cLBP) which
learns the dominant patterns from the decoded LBP is proposed for color images
recognition. This paper first proposes a relative similarity space (RSS) that
represents the color similarity between image channels for describing a color
image. Then, the decoded LBP which can mine the correlation information between
the LBP feature maps correspond to each color channel of RSS traditional RGB
spaces, is employed for feature extraction. Finally, a feature learning
strategy is employed to learn the dominant color-related patterns for reducing
the dimension of feature vector and further improving the discriminatively of
features. The theoretic analysis show that the proposed RSS can provide more
discriminative information, and has higher noise robustness as well as higher
illumination variation robustness than traditional RGB space. Experimental
results on four groups, totally twelve public color image datasets show that
the proposed method outperforms most of the LBP variants for color image
recognition in terms of dimension of features, recognition accuracy under
noise-free, noisy and illumination variation conditions.
