Primary visual cortex contributes to color constancy by predicting rather than discounting the illuminant: evidence from a computational study
This work addresses a fundamental problem in neuroscience and computer vision by providing computational evidence that could refine understanding of visual mechanisms and inspire more effective models.
The study tackled the unresolved role of the primary visual cortex (V1) in color constancy by building a neural model that learns illuminant prediction from natural images, finding that double-opponent neurons in V1 encode the illuminant rather than discount it, contradicting common hypotheses.
Color constancy (CC) is an important ability of the human visual system to stably perceive the colors of objects despite considerable changes in the color of the light illuminating them. While increasing evidence from the field of neuroscience supports that multiple levels of the visual system contribute to the realization of CC, how the primary visual cortex (V1) plays role in CC is not fully resolved. In specific, double-opponent (DO) neurons in V1 have been thought to contribute to realizing a degree of CC, but the computational mechanism is not clear. We build an electrophysiologically based V1 neural model to learn the color of the light source from a natural image dataset with the ground truth illuminants as the labels. Based on the qualitative and quantitative analysis of the responsive properties of the learned model neurons, we found that both the spatial structures and color weights of the receptive fields of the learned model neurons are quite similar to those of the simple and DO neurons recorded in V1. Computationally, DO cells perform more robustly than the simple cells in V1 for illuminant prediction. Therefore, this work provides computational evidence supporting that V1 DO neurons serve to realize color constancy by encoding the illuminant,which is contradictory to the common hypothesis that V1 contributes to CC by discounting the illuminant using its DO cells. This evidence is expected to not only help resolve the visual mechanisms of CC, but also provide inspiration to develop more effective computer vision models.