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Related Experiment Video

Updated: Sep 28, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep neural models for color classification and color constancy.

Alban Flachot^1,2, Arash Akbarinia^1,3, Heiko H Schütt^4,5

¹Abteilung Allgemeine Psychologie, Justus Liebig University, Giessen, Germany.

Journal of Vision

|March 30, 2022

Summary

This summary is machine-generated.

Deep neural networks achieve high color constancy, accurately perceiving object colors under different lighting. Performance varied with cues, with simpler networks showing representations closer to human color vision.

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Area of Science:

Computer Vision
Computational Neuroscience
Color Science

Background:

Color constancy is crucial for visual perception, enabling stable color recognition despite changing illumination.
Deep neural networks (DNNs) offer a powerful tool for modeling complex visual tasks like color constancy.

Purpose of the Study:

To train and evaluate DNNs for color constancy across diverse simulated scenes and illuminations.
To investigate the impact of visual cues on DNN color constancy performance.
To compare the internal color representations of different DNN architectures.

Main Methods:

Trained DNNs (ResNets, ConvNets, DeepCC) on 3D rendered scenes with varied shapes, Munsell chip reflectances, and natural illuminations.
Evaluated models on novel illuminations, assessing color constancy levels.
Analyzed network representations by gradually removing scene cues.

Main Results:

DNNs achieved high color constancy, particularly along the daylight locus.
Color constancy decreased as scene cues were progressively removed.
DeepCC exhibited color representations aligned with human color vision dimensions, unlike more complex ResNets.

Conclusions:

DNNs can effectively model color constancy, demonstrating robustness across varied conditions.
The level of color constancy is sensitive to the availability of visual cues.
Simpler DNN architectures may offer more interpretable insights into biological color perception mechanisms.