ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (5): 1128-1135.doi: 10.7544/issn1000-1239.2016.20148320

• 图形图像 • 上一篇    下一篇



  1. (天津大学电子信息工程学院 天津 300072) (
  • 出版日期: 2016-05-01
  • 基金资助: 

Texture Image Classification with Noise-Tolerant Local Binary Pattern

Ji Zhong Nie Linhong   

  1. (School of Electronic Information Engineering, Tianjin University, Tianjin 300072)
  • Online: 2016-05-01

摘要: 局部二值模式(local binary pattern, LBP)特征是一种简单有效的纹理特征描述符,但是它的抗噪声能力较差.针对这一问题,提出一种对噪声较为鲁棒的纹理特征表示方法——抗噪声的完整增强局部二值模式(noise-tolerant complete enhanced LBP, CELBP\+{NT}).该特征基于局部二值模式特征,对光照、旋转和噪声均具有较好的鲁棒性.其提取过程如下:1)根据LBP中各模式的结构和出现频率对特征中的模式重新分类,提出增强局部二值模式(enhanced LBP, ELBP)特征;2)添加差值的模值信息与中心像素信息,并根据图像尺寸自适应地调整其中的阈值,提出完整增强局部二值模式(complete ELBP, CELBP)特征;3)进一步将该特征进行多尺度下的表示,从而最终提出具有抗噪声能力的纹理特征——CELBP\+{NT}.通过在常用的纹理数据库上添加不同强度和不同类型噪声的情况进行实验,结果表明:CELBP\+{NT}不仅能够显著提升无噪声纹理图像的分类性能,而且对含有噪声的纹理图像分类也有显著的性能提高.

关键词: 局部二值模式, 图像噪声, 纹理图像分类, 特征提取, 多尺度分析

Abstract: The local binary pattern (LBP) is a simple and effective texture descriptor. However, it is very sensitive to image noise. To deal with this problem, we propose an efficient texture feature named noise-tolerant complete enhanced local binary pattern (CELBP\+{NT}) to enhance the discriminant ability against the noisy texture images. Derived from the local binary pattern, CELBP\+{NT} is robust to illumination, rotation and noise. Its feature extraction process involves the following three steps. First, different patterns in LBP are reclassified to form an enhanced LBP (ELBP) based on their structures and frequencies. Then, in order to describe the local feature completely and sufficiently, the difference of modulus value and center pixel information is added to ELBP to develop a complete ELBP feature, named CELBP. Meanwhile, the adaptive threshold of CELBP is determined by the image size. Finally, CELBP\+{NT} is proposed by using the favorable characteristics of multi-scale analysis on CELBP. The features are evaluated on the popular Outex database with different intensity and different types of noise. Extensive experimental results show that CELBP\+{NT} not only demonstrates better performance to a number of state-of-the-art LBP variants under no-noise condition, but also effectively improves the performance of texture classification containing noise due to its high robustness and distinctiveness.

Key words: local binary pattern (LBP), image noise, texture image classification, feature extraction, multi-scale analysis