Robust texture features based on undecimated dual-tree complex wavelets and local magnitude binary patterns
Robust texture features based on undecimated dual-tree complex wavelets and local magnitude binary patterns
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DOI：
10.1109/icip.2015.7351548
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
N. Anantrasirichai;J. F. Burn;D. Bull
中科院分区：
文献类型：
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作者：
N. Anantrasirichai;J. F. Burn;D. Bull
由于光照变化、模糊和噪声引起的图像退化会对分类性能产生显著影响，但在这些条件下，还没有表现良好的描述符。我们提出了一种新的方法来获得纹理特征，这些失真的鲁棒性，基于非抽取双树复小波变换（UDT-CWT）1。由于UDT-CWT提供了尺度之间的局部空间关系，我们可以直接创建表示小波系数局部相位的图像位平面。在丢弃一些最受模糊和噪声影响的最精细尺度之后，通过局部二进制模式（LBP）捕获UDT-CWT的幅度。所得二进制码字的直方图然后形成用于纹理分类的特征。结果表明，我们的方法优于现有的方法，声称是不变的功能退化。
Image degradation due to illumination change, blur and noise can have a significant influence on classification performance, and yet no descriptors that perform well under these conditions exist. We propose a novel method for obtaining texture features, robust to these distortions, based on an undecimated dual-tree complex wavelet transform (UDT-CWT)1. As the UDT-CWT provides a local spatial relationship between scales, we can straightforwardly create bit-planes of the images representing local phases of wavelet coefficients. Magnitudes of the UDT-CWT are captured via a local binary pattern (LBP), after discarding some of the finest scales that are most affected by the blur and noise. A histogram of the resulting binary code words then forms the features used in texture classification. Results show that our approach outperforms existing methods, that claim to be invariant to feature degradations.