自适应的Haar型LBP纹理特征提取算法研究  

An adaptive Haar LBP texture feature extraction algorithm

作　　者：刘天时[1] 肖敏敏[1] 李湘眷[1] 

机构地区：[1]西安石油大学计算机学院,陕西西安710065

出　　处：《计算机工程与科学》2015年第7期1381-1386,共6页Computer Engineering & Science

基　　金：国家自然科学基金资助项目(41301480);陕西省自然科学基金资助项目(2010JM8032);陕西省教育厅专项科研计划资助项目(14JK1573)

摘　　要：在提取纹理图像的Haar型LBP特征中,人为设定的判断阈值主观性强、局部性差,导致提取的纹理细节和边缘模糊、纹理图像的局部性易被忽略。为此,提出了一种自适应的Haar型LBP纹理特征提取算法。该算法在二值化Haar型特征时引入高斯加权矩阵,以此获得客观、符合纹理图像局部特征的自适应判断阈值和Haar型LBP特征。实验结果表明,该算法能够有效地避免人为设定阈值对纹理特征的影响,可以准确地描述图像的纹理特征,Brodatz标准纹理库分类的正确率也得到了进一步的提高。Due to strong subjectivity and poor locality of the artificial setting judgment threshold, in the process of extracting the Haar local binary texture （LBP）, the extracted texture details and edges are not clear and the locality of texture image may be ignored. Therefore, we propose an adaptive Haar local binary pattern texture feature extraction algorithm, in which the Gaussian weighted matrix is intro- duced when the Haar characteristic is binarized. Subsequently the adaptive judgment threshold and the Haar local binary pattern which are objective and conform to the locality of texture image can be extrac- ted. Experimental results show that the proposed algorithm can effectively avoid the influence of the ar- tificial judgment threshold on texture feature and accurately describe the texture feature of images. Be- sides, the classification accuracy for Brodatz texture datasets can also be further improved.

关 键 词：纹理特征 HAAR特征 LBP 高斯加权矩阵 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]