Many representation methods were built for gray image textures. However, they are not effective for color textures in general. To alleviate this problem, in this paper we propose a novel Compact Interchannel Sampling Difference Descriptor (CISDD) for color texture classification. In particular, considering sampling-based method can capture more directional information, we first use a heavy-tailed distribution, t-distribution to generate sample points in the image patch to calculate the micro-block difference. Then we model the interchannel relationship of color texture image by using dense micro-block differences. Furthermore, we utilize principal component analysis (PCA) to reduce the dimensions of the features encoded by the Fisher vector, and construct a Compact Interchannel Sampling Difference Descriptor (CISDD) for representing color texture image. Finally, experimental results on five published standard texture datasets (KTH-TIPS, VisTex, CUReT, USPTex and Colored Brodatz) reveal that CISDD is effective and outperforms thirteen representative color texture classification methods.

中文翻译：

---

紧凑的通道间采样差异描述符，用于颜色纹理分类

建立了许多用于灰度图像纹理的表示方法。但是，它们通常对颜色纹理无效。为了缓解这个问题，本文提出了一种用于颜色纹理分类的新颖的紧凑通道间采样差异描述符（CISDD）。特别地，考虑到基于采样的方法可以捕获更多的方向信息，我们首先使用重尾分布，t分布在图像补丁中生成采样点以计算微块差异。然后，我们利用密集的微块差异对色彩纹理图像的通道间关系进行建模。此外，我们利用主成分分析（PCA）来缩小由Fisher向量编码的特征的维数，并构造一个紧凑的通道间采样差异描述符（CISDD）来表示彩色纹理图像。最后，对五个已发布的标准纹理数据集（KTH-TIPS，VisTex，CUReT，USPTex和Colored Brodatz）的实验结果表明，CISDD是有效的，并且胜过了十三种代表性的颜色纹理分类方法。