摘要:
局部二值模式(Local binary pattern, LBP)是一种像素层的局部特征，编码了中心像素与周围像素之间的相对强度值。由于其理论简单、计算高效，具有较高的特征辨别力和较低的计算复杂度，因此在纹理分类、人脸识别和表情检测等计算机视觉领域得到了广泛的应用，进而提出了许多LBP扩展方法，在辨别性、鲁棒性和计算效率方面有了很大提高。鉴于LBP的理论意义和使用价值，为使研究者对LBP有一个更为全面的认识和了解，便于深入研究，在前面综述文献的基础上进一步对LBP及其扩展模式进行综述，归纳了LBP改进模式的结构和LBP在不同领域中的具体应用，分析了基本LBP方法及其扩展方法结构和其优缺点，在辨别性、低维性、不变性方面与局部描述符进行了对比，总结了LBP扩展模式的应用领域。最后指出LBP扩展模式有待继续完善和发展的研究方向。
Abstract:
Local Binary Pattern (LBP) is a local feature on pixel level, which encodes the relative strength among the center pixel and the surrounding pixels. Because of its simple principle, high computational efficiency and feature discrimination, and low computational complexity, it has been popular in the computer vision field, such as in texture analysis field, face recognition field, expression detection field, and so on. Moreover, a number of extended methods about LBP, which are greatly improved in discrimination, robustness, and computational efficiency, are proposed. In view of the theoretical and practical value of LBP, in order to make the researcher have more comprehensive understanding and further study about LBP, this paper overviews LBP and its extended, and summarizes the specific application in different fields, and analyses the structure, as well as advantages and disadvantages. Then, some comparisons are done between LBP and other local descriptors in the aspect of identification, low dimension and invariance, and some conclusions are done about the application field of the extended LBP models. Finally, the future research directions of the extended LBP models are proposed.