Fusing DTCWT and LBP Based Features for Rotation, Illumination and Scale Invariant Texture Classification
Fusing DTCWT and LBP Based Features for Rotation, Illumination and Scale Invariant Texture Classification
复制标题
融合基于 DTCWT 和 LBP 的特征进行旋转、照明和尺度不变纹理分类
DOI：
10.1109/access.2018.2797072
复制
发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Yang, Guowei
中科院分区：
文献类型：
--
作者：
Yang, Peng;Zhang, Fanlong;Yang, Guowei
Classification of texture images with different orientation, illumination, and scale changes is a challenging problem in computer vision and pattern recognition. This paper proposes two descriptors and uses them jointly to fulfill such task. One can obtain an image pyramid by applying dual-tree complex wavelet transform (DTCWT) on the original image, and generate local binary patterns (LBP) in DTCWT domain, called LBPDTCWT, as local texture features. Moreover, log-polar (LP) transform is applied on the original image, and the energies of DTCWT coefficients on detail subbands of the LP image, called LPDTCWTE, are taken as global texture features. We fuse the two kinds of features for texture classification, and the experimental results on benchmark data sets show that our proposed method can achieve better performance than other the state-of-the-art methods.
