具有旋转鲁棒性的纹理谱描述子  

Robust rotation-invariant texture spectrum descriptor

作　　者：陈刚[1] 陈晓云[1] 

机构地区：[1]福州大学数学与计算机科学学院,福州350108

出　　处：《中国图象图形学报》2013年第2期214-218,共5页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61070020)

摘　　要：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%.

关 键 词：中心对称局部二值模式 旋转鲁棒性 纹理谱描述子 图像特征 图像识别 特征提取 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]