四川大学学报（自然科学版）2012，Vol.49Issue(5)：995-1002,8.DOI:10.3969/j.issn.0490-6756.2012.05.011
一种新型的基于自适应局部二值模式的纹理分类算法
A novel texture classification algorithm based on adaptive local binary pattern
摘要
Abstract
The paper define a new adaptive local binary pattern, abbreviate ALBP, which employs the u-niformity and similarity of texture patterns to classify different patterns and then re-label them to enhance the robustness under different illuminant, noise, scaling, rotation, and translation. Combing with differential operation, a local region is represented not only by its local difference sign but also through magnitude matrix. Finally, the two part eigenvalues are concatenated into an enhancement feature vectors, and used for texture classification. The classification was conducted using a nearest neighborhood classifier in the computed feature space with Chi-square as a dissimilarity measure. Experiments and comparisons show that the proposed method has better result under different rotation, illuminant and noise conditions.关键词
纹理特征提取/自适应局部二值模式/差分二值矩阵/差分绝对值矩阵Key words
texture feature extraction/ adaptive LBP/ difference sign matrix/ difference magnitude matrix分类
信息技术与安全科学引用本文复制引用
龚家强,李晓宁..一种新型的基于自适应局部二值模式的纹理分类算法[J].四川大学学报（自然科学版）,2012,49(5):995-1002,8.基金项目
四川省科技厅苗子工程项目(2011-053) （2011-053）
可视化计算与虚拟现实四川省重点实验室课题(PJ201103) （PJ201103）
四川师范大学研究生科研创新基金项目(2011-022) （2011-022）
