Rotation invariant color texture classification using multiple sub-DLBPs

•DLBP descriptor is presented to characterize the color texture information.•Create an index image to depict the color information.•Combine the color index value with DLBP by estimating their joint distribution to construct a MS_DLBP descriptor.•Discuss about the effect of different color spaces in color texture classification using the proposed MS_DLBP descriptor.•Experimental results show that the MS_DLBP descriptor is rotation invariant and is more discriminative.

It is known that the rotations of real-world color textures will vary arbitrarily. This paper presents a novel, simple, yet powerful method for rotation-invariant color texture classification. Firstly, we define a Distance-based Local Binary Pattern (DLBP) descriptor to characterize the color texture. By learning the joint distribution of the rotation-invariant DLBP and color intensity information, we define our Multiple Sub-DLBPs   (MS_DLBPMS_DLBP) descriptor. The MS_DLBPMS_DLBP features defined in this paper are invariant to rotation. Here, we also compared seven important color spaces in terms of their effectiveness in our proposed MS_DLBPMS_DLBP approach. The experimental results on the Outex and CUReT databases show the defined DLBP descriptor performs better than the existing color LBP descriptors and the proposed MS_DLBPMS_DLBP approach is very robust to rotation invariance and outperforms state-of-the-art texture analysis methods. Also, HSV color space is shown to outperform the other color spaces in many cases.