FOCAL: Feature-Oriented Cellular Automata Learning for Convolution-Free Image Classification

State-of-the-art image classification systems utilize powerful machine-learning-based tools such as Convolutional Neural Networks (CNNs). These networks can achieve high recognition accuracies, but suffer from a black-box problem where the inner workings are incomprehensible by humans that seek to use them. In this paper, a Feature-Oriented Cellular Automata Learning (FOCAL) system is developed to extend traditional gradient-filter-based methods by implementing a Cellular Automata (CA) reasoner utilizing rule-based primitives for determining mutual agreement between neighboring pixels. This novel method is demonstrated to identify features more accurately than standard filter methods and produce classification results that are competitive with typical CNNs, while also allowing a-priori definition of important features facilitating explainable feature classification decision processes. Experiments spanning a variety of influential factors indicate that rebaselining and normalization are vital to the success of the CA-based approach. Furthermore, within certain models, the use of CA is shown to reduce computational demand by over 90% while incurring only a 2% reduction in classification accuracy. Finally, the scalability of the FOCAL system is investigated using the CIFAR-10 dataset and contemporary Deep Neural Networks, and shown to encourage promising avenues of research into explainability while reducing computational processing demands.