Reinforcement Learning in Image Classification: A Review

Image classification experiments face several problems related to image specifications, size of samples, and classification accuracy. The image classification related issues motivated the researchers to use Reinforcement Learning (RL) with image classification experiments to enhance it. RL is a self-learning approach where machines can learn from experience. This paper aims to study the influence of RL on image classification trials. Besides, exploring the main issues in RL-based image classification. The authors reviewed and analyzed 15 papers related to applying RL techniques on image classification. Major studies used RL techniques to solve image classification issues, e.g., high dimensionality and misclassifications. Whereas other studies developed RL-based image modification methods used for processing images, such as rotating and cropping. This work concludes that the RL has a beneficial impact on solving classification issues and modifying images. However, some areas should be further considered, such as applying RL techniques in image classification in real-world automation projects.