A Bio-inspired Quaternion Local Phase CNN Layer with Contrast Invariance and Linear Sensitivity to Rotation Angles

Abstract Deep learning models have been particularly successful with image recognition using Convolutional Neural Networks (CNN). However, the learning of a contrast invariance and rotation equivariance response may fail even with very deep CNNs or by large data augmentations in training. We were inspired by the V1 visual features of the mammalian visual system to emulate as much as possible the early visual system and add more invariant capacities to the CNN. We present a new quaternion local phase convolutional neural network layer encoding three local phases. We present two experimental setups: An image classification task with four contrast levels, and a linear regression task that predicts the rotation angle of an image. In sum, we obtain new patterns and feature representations for deep learning, which capture illumination invariance and a linear response to rotation angles.