Color image recognition with color opponency-based filters and two-layer spiking neural networks

Abstract

Spiking neural networks (SNNs) are the basis of low-power neuromorphic computing systems and hardware. Most previous studies on unsupervised SNNs have tested their ability in grayscale image classification. In this study, we mainly propose color opponency-based filters, inspired by retinal color vision, for color image classification with an unsupervised two-layer SNN and a simple linear classifier. We demonstrate that the color opponency-based filters in multiple visual pathways are more effective than conventional grayscale transform methods in a color image classification task with ETH-80 dataset. Our results suggest that biological color vision mechanisms can expand the potential of shallow SNN models.