Texture is normally represented by aggregating local features based on the assumption of spatial homogeneity. Effective texture features are always the research focus even though both hand-crafted and deep learning approaches have been extensively investigated. Motivated by the success of Bilinear Convolutional Neural Networks (BCNNs) in fine-grained image recognition, we propose to incorporate the BCNN with the Pair-wise Difference Pooling (i.e. BCNN-PDP) for texture classification. The BCNN-PDP is built on top of a set of feature maps extracted at a convolutional layer of the pre-trained CNN. Compared with the outer product used by the original BCNN feature set, the pair-wise difference not only captures the pair-wise relationship between two sets of features but also encodes the difference between each pair of features. Considering the importance of the gradient data to the representation of image structures, we further generalise the BCNN-PDP feature set to two sets of feature maps computed from the original image and its gradient magnitude map respectively, i.e. the Fused BCNN-PDP (F-BCNN-PDP) feature set. In addition, the BCNN-PDP can be applied to two different CNNs and is referred to as the Asymmetric BCNN-PDP (A-BCNN-PDP). The three PDP-based BCNN feature sets can also be extracted at multiple scales. Since the dimensionality of the BCNN feature vectors is very high, we propose a new yet simple Block-wise PCA (BPCA) method in order to derive more compact feature vectors. The proposed methods are tested on seven different datasets along with 21 baseline feature sets. The results show that the proposed feature sets are superior, or at least comparable, to their counterparts across different datasets.

中文翻译：

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使用基于双线性卷积神经网络的成对差分池进行纹理分类。


纹理通常是通过基于空间同质性的假设聚合局部特征来表示的。尽管手工制作和深度学习方法都已被广泛研究，但有效的纹理特征始终是研究重点。受双线性卷积神经网络（BCNN）在细粒度图像识别方面成功的启发，我们建议将 BCNN 与成对差分池（即 BCNN-PDP）结合起来进行纹理分类。 BCNN-PDP 构建在预训练 CNN 卷积层提取的一组特征图之上。与原始 BCNN 特征集使用的外积相比，pair-wise Difference 不仅捕获了两组特征之间的成对关系，而且还编码了每对特征之间的差异。考虑到梯度数据对图像结构表示的重要性，我们进一步将 BCNN-PDP 特征集推广为分别根据原始图像及其梯度幅值图计算的两组特征图，即 Fused BCNN-PDP (F- BCNN-PDP）特征集。此外，BCNN-PDP可以应用于两个不同的CNN，被称为非对称BCNN-PDP（A-BCNN-PDP）。三个基于 PDP 的 BCNN 特征集也可以在多个尺度上提取。由于 BCNN 特征向量的维数非常高，我们提出了一种新的但简单的 Block-wise PCA (BPCA) 方法，以导出更紧凑的特征向量。所提出的方法在 7 个不同的数据集以及 21 个基线特征集上进行了测试。结果表明，所提出的特征集优于不同数据集的对应特征集，或者至少具有可比性。