融合CLBP和局部几何特征的纹理目标分类

Texture target classification with CLBP and local geometric features

针对基于LBP的许多改进方法需要提前训练,对旋转和照明变化鲁棒性较差的特点,本文通过融合CLBP和图像表面的局部几何不变特征提出了一种新的纹理分类方法。该算法首先计算图像表面的局部几何不变特征,然后对其进行量化和编码。其次,再将编码结果与CLBP直方图进行融合。本文提出的算法能够同时提取图像的宏观和微观特征,且具有不明显增加特征维度,无需提前训练,对图像的旋转和光照变化保持不变的特点。在两个标准纹理数据库上进行实验验证,结果表明,本文算法与其它算法相比在分类精度和鲁棒性上都有明显的提高。

For the problems of needing pre-training and poor robustness to rotation and illumination changes of various improved algorithms based on local binary pattern(LBP), this paper presents a new texture classification algorithm by integrating the completed local binary pattern(CLBP) and the local geometric invariant features of the image surface. In our algorithm, the local geometric invariant features are first computed. Then the computed results are further quantified and encoded to make combination with the CLBP histogram. The proposed algorithm can extract image macroscopic and microscopic features simultaneously, and it has the properties of not significantly increasing feature dimension, without pre-training, and invariance to the rotation and illumination changes. Experimental verifications are conducted on two standard texture databases, and the results demonstrate that the proposed algorithm outperforms the comparative classification algorithms in classification accuracy and robustness.