A biologically inspired visual integrated model for image classification

Abstract

In this paper, we propose a biologically inspired visual integrated model for image classification, called VMVI-CNN. Motivated in part by recent neuroscience progress in revealing integrated functions of human visual system, two bio-inspired visual mechanisms (the visual memory decay mechanism and the visual interaction mechanism) are proposed and built within the VMVI-CNN to (1) control the feature information passing through, and (2) increase the richness of feature information. The proposed method is tested on three benchmark datasets (MNIST, Cifar-10, and Mini-ImageNet) and a real-world industrial dataset. The results demonstrate that the new model can extract distinctive features and exhibit a better recognition performance than the current state-of-the-art approaches.