基于Haar特性的LBP纹理特征

图像纹理特征能够反映图像的灰度分布程度、对比度、空间分布和图像的内在变化特性,在确保较低计算复杂度的前提下,有效提取深层次的图像纹理信息是其研究的难点问题。针对这一问题,从相邻区域的统计特征分析入手,提出了一种Haar型特性局部二元模式(Haar local binary pattern,简称HLBP)的图像纹理特征提取方法。鉴于Haar型特征运算简单、快捷,统计局部特征有效、可靠,将其引入LBP中。该方法首先给出8组Haar型特征编码模式,按照局部二元模式(local binary pattern,简称LBP)统计图像局部纹理特征,因采用局部区域统计方法能够有效降低噪声的影响；其次,为了进一步提高图像纹理特征的有效呈现,结合Gabor小波滤波在不同方向、不同尺度对灰度水平图像进行特征提取,以增强纹理有效提取的性能,提高不变特征的稳健性；最后,通过4组对比实验验证了该方法的可行性。实验分别在标准的 Brodatz 正常分块纹理库测试集、分块且缩放 Brodatz 纹理库测试集、分块且旋转Brodatz纹理库测试集以及Yale B扩展的非均匀光照条件人脸库测试集上进行。实验结果表明,该方法能够有效地表达图像的纹理特征。

LBP Texture Feature Based on Haar Characteristics

The image texture feature reflects some characteristics of the degree of gray distribution, contrast, spatial distribution and changes in the intrinsic properties of image. Under the premise of lower computational complexity, it is a difficult problem for effective feature extraction of deep level image texture. Aiming to solve this problem, this paper, from the analysis of statistical characteristics of adjacent regions, proposes an image texture features extraction method, which is based on Haar local binary pattern (HLBP). In view of simple and quick operating of Haar-like features, effective and reliable to local features statistic, Haar is inducted into LBP. This method first shows eight groups of Haar feature encoding models, which calculate the local texture features of image in accordance with local binary pattern (LBP). Through this method, it can reduce the noise impact effectively. Then, in order to further enhance the effective representations of the image texture features, the method combines with Gabor wavelet filters in different directions and different scales of gray-level image feature extraction, which intends to enhance the effective performance of the texture feature extraction. Finally, through four comparing experiments, this method has proven to be a feasible tool for analyzing image texture features.