Capsule network (CapsNet) is a novel architecture that takes into account the hierarchical pose relationships between object parts, which had achieved desirable results on image classification. EM-Routing (EM-R) used in CapsNet is the process of assigning child capsules (parts) to each parent capsule (objects) based on a level of agreement, which is similar to the fuzzy clustering process. However, CapsNet still struggles with backgrounds and the presence of noise. In this paper, a new routing algorithm based on a weighted capsule fuzzy gaussian model (WCFGM-R) and a pose loss function are proposed. The proposed algorithm aims to prohibit atypical child capsules from contaminating the parent capsules by incorporating the activations of capsules in a lower layer as weights that play the role of precision. The pose loss provides the best inter-class separation and improves the ability of pattern classification. Indeed, the experimental analyses demonstrate that CapsNet with WCFGM-R outperforms the CapsNet with EM-R in which it shows excellent results on three datasets (MNIST-bg-img, MNIST-bg-rnd, and CIFAR10).

胶囊网络（CapsNet）是一种新颖的体系结构，它考虑了对象部分之间的分层姿势关系，该关系在图像分类上已取得理想的结果。CapsNet中使用的EM路由（EM-R）是基于一致程度将子级容器（零件）分配给每个父级容器（对象）的过程，这类似于模糊聚类过程。但是，CapsNet仍在为背景和噪音而挣扎。提出了一种基于加权胶囊模糊高斯模型（WCFGM-R）和姿态损失函数的新型路由算法。所提出的算法旨在通过在较低层中合并胶囊的激活作为权重发挥作用，从而防止非典型儿童胶囊污染母胶囊。姿态损失可提供最佳的类间分隔，并提高模式分类的能力。确实，实验分析表明，带有WCFGM-R的CapsNet优于带有EM-R的CapsNet，在CapsNet上，它在三个数据集（MNIST-bg-img，MNIST-bg-rnd和CIFAR10）上显示了出色的结果。