  The main aim of this paper is to propose a color texture classification
approach which uses color sensor information and texture features jointly. High
accuracy, low noise sensitivity and low computational complexity are specified
aims for our proposed approach. One of the efficient texture analysis
operations is local binary patterns. The proposed approach includes two steps.
First, a noise resistant version of color local binary patterns is proposed to
decrease sensitivity to noise of LBP. This step is evaluated based on
combination of color sensor information using AND operation. In second step, a
significant points selection algorithm is proposed to select significant LBP.
This phase decreases final computational complexity along with increasing
accuracy rate. The Proposed approach is evaluated using Vistex, Outex, and KTH
TIPS2a data sets. Our approach has been compared with some state of the art
methods. It is experimentally demonstrated that the proposed approach achieves
highest accuracy. In two other experiments, result show low noise sensitivity
and low computational complexity of the proposed approach in comparison with
previous versions of LBP. Rotation invariant, multi resolution, general
usability are other advantages of our proposed approach. In the present paper,
a new version of LBP is proposed originally, which is called Hybrid color local
binary patterns. It can be used in many image processing applications to
extract color and texture features jointly. Also, a significant point selection
algorithm is proposed for the first time to select key points of images.
