Modified Algorithm of Capsule Network for Classifying Small Sample Image

Abstract

In order to address the problem that the capsule network can not classify complex small sample images effectively, a classification model is proposed on the basis of fusing the improved Darknet with the capsule network. Firstly, the Darknet is upgraded containing both the shallow level extractor and the deep level extractor. The shallow level extractor adopts a 5×5 convolution kernel to capture long-distance edge contour features and the deep level extractor uses a 3×3 convolution kernel to capture deeper semantic features. Then, the extracted edge features and semantic features are fused to preserve effective features of images. Next, the capsule network is used to vectorize these effective features to work out the loss of spatial representation. Finally, L2 regularization is added in the loss function to avoid the over-fitting. Experimental results show that, on the small sample dataset, the classification accuracy of the proposed model is 28.51 percentage points and 24.40 percentage points higher than that of the models of the capsule network and the DCaps respectively, 21.57 percentage points and 18.02 percentage points higher than that of the ResNet50 and the Xception respectively. Hence it suggests that the method proposed in this paper gains a better performance in classifying complex small sample images. Meanwhile, on the large sample dataset, the classification accuracy of the proposed model has also been improved to a certain extent.

Keywords

• |small sample image|capsule network|darknet|l2 regularization|image classification