Capsule networks for image classification: A review

Abstract

Over the past few years, the computer vision domain has evolved and made a revolutionary transition from human-engineered features to automated features to address challenging tasks. Computer vision is an ever-evolving domain with its roots deeply correlated with neuroscience; any new findings that trigger a more intuitive understanding and working of the human visual system generally impact the design strategy of computer vision algorithms. The convolutional neural network is one such algorithm that is currently the de facto standard for most computer vision tasks such as image classification, object detection, image segmentation, etc. As convolutional neural networks are associated with inherent constraints such as the requirement for an immense amount of labeled data and an inefficient data routing policy, capsule networks could be a viable alternative. Upheld by the backpropagation and the dynamic routing algorithm, the capsule network has set the new paradigm for developing reliable computer vision algorithms. Despite the phenomenal theoretical backing from neuroscience and the groundbreaking performance on benchmark datasets, the lack of information concerning the conception and working of capsule networks becomes the major impediment to adopting them for computer vision algorithms. This paper presents a concise overview of capsule network-based classification architectures, routing algorithms, performance analysis, limitations, and future scope, helping the research community to adopt capsule networks at the forefront of modern computer vision research.