PT Unknown
AU Ivet Rafegas
   Maria Vanrell
TI Color representation in CNNs: parallelisms with biological vision
BT ICCV Workshop on Mutual Benefits ofr Cognitive and Computer Vision
PY 2017
DI 10.1109/ICCVW.2017.318
AB Convolutional Neural Networks (CNNs) trained for object recognition tasks present representational capabilities approaching to primate visual systems [1]. This provides a computational framework to explore how image featuresare efficiently represented. Here, we dissect a trained CNN[2] to study how color is represented. We use a classical methodology used in physiology that is measuring index of selectivity of individual neurons to specific features. We use ImageNet Dataset [20] images and synthetic versionsof them to quantify color tuning properties of artificial neurons to provide a classification of the network population.We conclude three main levels of color representation showing some parallelisms with biological visual systems: (a) a decomposition in a circular hue space to represent single color regions with a wider hue sampling beyond the firstlayer (V2), (b) the emergence of opponent low-dimensional spaces in early stages to represent color edges (V1); and (c) a strong entanglement between color and shape patterns representing object-parts (e.g. wheel of a car), objectshapes (e.g. faces) or object-surrounds configurations (e.g. blue sky surrounding an object) in deeper layers (V4 or IT).
ER