Evolving Granular Convolutional Network for Image Flow Classification

Abstract

Recent advances in machine learning for computer vision and image classification have presented significant challenges, such as interpretability of deep neural network models and ability for continuous learning in dynamic environments. This paper introduces Convolutional Evolving Granular Neural Networks (CEGNN). A CEGNN combines the feature extraction components of a VGG-16 convolutional net with an evolving granular neural network (EGNN) toward advancing the understanding and applications of incremental learning in computer vision, particularly in image recognition and classification. An incremental algorithm is incorporated to the CEGNN to improve model interpretability and continuous learning. Experimental results show that the CEGNN is efficient and competitive in image classification, achieving an accuracy of 78.9% and a precision of 79.0% in a 10-class problem. This opens research avenues in certain applications, such as those dealing with medical images, satellite images, and image-based closed-loop autonomous systems.