Research and Improvement of Dynamic Routing Based on Capsule Network

Capsule Network(CapsNet) has the advantage of compensating for the loss of spatial information and rotation invariance in convolutional neural networks.Therefore, they have been widely used in many fields such as image classification, target detection, and text detection.However, capsule networks still face some problems, such as having a large amount of parameters and low classification accuracy.This paper proposes a novel capsule network classification model that uses the dot-product attention mechanism to construct the graph convolutional layer, thereby selecting high-quality features to facilitate correct classification of the image.To obtain the spatial dependence between capsules within the same layer, the dot-multiplication attention mechanism is used to construct a fully connected graph between capsules of the same levels.The predicted capsules with large influencing factors are then used for feature clustering.In this way, the proposed model can reduce the number of parameters and improve model performance.At the same time, the residual network is added to the feature extraction process for the extraction of higher-dimensional features in order to optimize the quality of the capsule, which can not only improve the feature expression ability of the model but also restrain its size.The experimental results prove that when the number of parametersis less thanthat the number of multiple capsule network variants, the proposed model achieves 99.74%, 95.02%, 91.78% and 95.65% accuracy on the MNIST, FashionMNIST, CIFAR10, and SVHN datasets, respectively, making the proposed model more accurate than MS-CapsNet, TextCaps, AR CapsNet, FSc-CapsNet, and DA-CapsNet models.