CapsNet comparative performance evaluation for image classification

Understand

Image classification has become one of the main tasks in the field of computer vision technologies.

• In this context, a recent algorithm called CapsNet that implements an approach based on activity vectors and dynamic routing between capsules may overcome some of the limitations of the current state of the art artificial neural networks (ANN) classifiers, such as convolutional neural networks (CNN).
• In this paper, we evaluated the performance of the CapsNet algorithm in comparison with three well-known classifiers (Fisher-faces, LeNet, and ResNet).
• We tested the classification accuracy on four datasets with a different number of instances and classes, including images of faces, traffic signs, and everyday objects.