Efficient Coding in Human Vision as a Useful Bias in Computer Vision and Machine Learning

Abstract

Interdisciplinary research in human vision has greatly contributed to the current state-of-the-art in computer vision and machine learning starting with low-level topics such as image compression and image quality assessment up to complex neural networks for object recognition. Representations similar to those in the primary visual cortex are frequently employed, e.g., linear filters in image compression and deep neural networks. Here, we first review particular nonlinear visual representations that can be used to better understand human vision and provide efficient representations for computer vision including deep neural networks. We then focus on i2D representations that are related to end-stopped neurons. The resulting E-nets are deep convolutional networks, which outperform some state-of-the-art deep networks. Finally, we show that the performance of E-nets can be further improved by using genetic algorithms to optimize the architecture of the network.