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：

---

用于颜色纹理分类的紧凑通道间采样差异描述符


针对灰度图像纹理建立了许多表示方法。然而，它们通常对于颜色纹理无效。为了缓解这个问题，在本文中，我们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）。特别是，考虑到基于采样的方法可以捕获更多方向信息，我们首先使用重尾分布、t分布在图像块中生成样本点来计算微块差异。然后，我们利用密集微块差异对彩色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）来降低Fisher向量编码的特征的维度，并构造一个紧凑通道间采样差异描述符（CISDD）来表示彩色纹理图像。最后，在五个已发布的标准纹理数据集（KTH-TIPS、VisTex、CUReT、USPTex 和 Colored Brodatz）上的实验结果表明，CISDD 是有效的，并且优于 13 种代表性的颜色纹理分类方法。