Exploring the performance of LBP-capsule networks with K-Means routing on complex images

Abstract

Capsule Networks (CapsNets) were proposed to mitigate the shortcomings of Convolutional Neural Networks (CNNs) such as invariance. Even though they have achieved equivariance, they fail to perform on the recognition of complex images and images with varied backgrounds such as CIFAR-10. Real-life images such as those found in plant disease datasets (aside from being complex with varied backgrounds) pose additional challenges such as class imbalance and the availability of a smaller number of annotated datasets. The original CapsNet uses CNNs as feature extractors, SoftMax for normalization, and dynamic routing (DR) to enable active capsules to make predictions resulting in the activation of higher-level capsules. However, CNNs do not serve as superior texture extractors and SoftMax restrains capsules from forming optimal coupling during routing. In this paper, we propose the use of an efficient texture descriptor (Local Binary Pattern -LBP), sigmoid function, and k-means routing respectively in place of CNN, SoftMax, and dynamic routing. We evaluate our model on six publicly available datasets; MNIST, fashion-MNIST, CIFAR-10, tomato, maize, and citrus datasets. Experimental results show that the proposed model generates fewer parameters and performs comparably well with the state-of-the-art multi-lane capsule networks on complex images.

Keywords

• Local binary pattern
• Capsule network
• Convolutional neural networks
• Dynamic routing
• K-Means
• Plant disease