为克服传统CS-LBP(Center-Symmetric Local Binary Patterns)描述子旋转鲁棒性较差的问题,从人眼视觉角度考虑,提出一种新的CS-LBP纹理谱描述子。将纹理模式的旋转变化与字符串的移位操作相结合,基于纹理模式等价类的思想,设计了更有效的方法提升新描述子的抗旋转能力。实验结果表明,新描述子及方法具有更强的旋转鲁棒性,使旋转纹理图像的聚类准确率提高了9%~38%。

In order to overcome the weakness of tradition CS-LBP descriptor in rotation-invariant, in this paper, we propose a new CS-LBP texture spectrum descriptor from the perspective of the human visual. It integrates the rotation of texture pattern with the shift operation of string. Based on the concept of texture pattern equivalent, a more effective method is designed to enhance the anti-rotation ability of the new descriptor. Experimental results show that the new descriptor and method have with more rotation robustness, and improve the clustering accuracy of rotary texture image by 9% to 38%.