This paper proposes a novel feature representation method for color images, namely quaternionic extended local binary pattern (QxLBP) with adaptive structural pyramid pooling (ASPP). First, we propose a QxLBP operator to encode local neighboring information and complementary modulus and phase information in the quaternion domain of color images. In QxLBP, an extended quaternionic representation (EQR) is proposed which introduces an information term into the real part of a quaternion. The resulting EQR enables us to flexibly encode discriminative features and handle multichannel image data. Second, we propose ASPP as a multiresolution pooling way to aggregate local features. Unlike the traditional spatial pyramid pooling which is sensitive to image rotation and spatial changes, ASPP is structure-oriented pooling which can adaptively aggregate the encoded features into multiresolution histogram representations. Experiments on four benchmark image datasets demonstrate that the proposed method achieves the state-of-the-art performance for color texture classification and scene categorization.

本文提出了一种新颖的彩色图像特征表示方法，即具有自适应结构金字塔池（ASPP）的四元数扩展局部二进制图案（QxLBP）。首先，我们提出了一个QxLBP算子，用于在彩色图像的四元数域中编码局部相邻信息以及互补模量和相位信息。在QxLBP中，提出了扩展的四元数表示（EQR），该信息将信息项引入四元数的实部。产生的EQR使我们能够灵活地编码区分特征并处理多通道图像数据。其次，我们提出ASPP作为聚合本地特征的多分辨率池方法。与传统的空间金字塔池化不同，后者对图像旋转和空间变化敏感，ASPP是面向结构的池，可以将编码后的特征自适应地聚合为多分辨率直方图表示形式。在四个基准图像数据集上进行的实验表明，该方法在色彩纹理分类和场景分类方面达到了最先进的性能。