Convolutional Color Constancy (1507.00410v2)

Abstract: Color constancy is the problem of inferring the color of the light that illuminated a scene, usually so that the illumination color can be removed. Because this problem is underconstrained, it is often solved by modeling the statistical regularities of the colors of natural objects and illumination. In contrast, in this paper we reformulate the problem of color constancy as a 2D spatial localization task in a log-chrominance space, thereby allowing us to apply techniques from object detection and structured prediction to the color constancy problem. By directly learning how to discriminate between correctly white-balanced images and poorly white-balanced images, our model is able to improve performance on standard benchmarks by nearly 40%.

• The paper introduces a discriminative approach by reframing color constancy as a 2D localization task in log-chrominance space.
• The method employs pyramid convolution and augmented image channels to achieve up to a 39% reduction in median angular error.
• This approach paves the way for real-time imaging applications and advances in low-level computer vision tasks.

Convolutional Color Constancy: A Review

The paper "Convolutional Color Constancy" by Jonathan T. Barron introduces a novel perspective on solving the problem of color constancy using a discriminative learning approach. Traditional methods in color constancy involve modeling the statistical regularities of the colors of natural objects and illumination generatively. In contrast, this paper reframes the problem as a discriminative task leveraging techniques from object detection and structured prediction.

Problem Formulation and Methodology

Color constancy, the task of inferring the color of the light that illuminated a scene, is reformulated in this paper as a 2D spatial localization task in a log-chrominance space. The main challenge is to estimate the illumination $L$ given an image $I$ so that a white-balanced image $W = I/L$ can be achieved. By defining log-chrominance measures, the task reduces to estimating a simple linear constraint, effectively translating the problem into a localization task.

The paper introduces a novel approach named Convolutional Color Constancy (CCC), which applies convolutional neural networks (CNNs) like structured prediction techniques for the discriminative training of color constancy tasks. The model leverages the chrominance properties of the input image, constructing histograms that are convolved with learnable filters to deduce the likely white-balance state of the image.

Key Contributions

1. Discriminative Learning: Unlike traditional models that foster generative methods, CCC directly trains a discriminator to differentiate between well-balanced and poorly balanced images. This approach improves the learning ability of the model by targeting the task-specific structure instead of general color distribution.
2. Log-Chrominance Space: The redefinition of the problem space in terms of log-chrominance allows the method to frame the task as a localization issue akin to sliding window object detection problems.
3. Efficient Filtering via Pyramid Convolution: The paper employs pyramid filtering methods to perform efficient log-chrominance histogram evaluations. This method enables fast convolution operations, which are crucial for real-time applications.
4. Augmented Image Channels: By incorporating various spatial representations of the input image, the model accounts for local textures and edges, enhancing accuracy without sacrificing computational efficiency.

Results

On benchmark datasets, CCC demonstrates significant reductions in error rates compared to previous state-of-the-art algorithms. On the Color Checker dataset, CCC achieves a 39% reduction in median angular error, while on the dataset by Cheng et al., a notable 22% improvement in average error is recorded. These results underscore the efficacy of the discriminative approach over generative models, particularly in tasks inherently oriented toward classification and localization.

Implications and Future Outlook

The innovative use of discriminative techniques in CCC marks a significant shift in the paradigm for image processing tasks like color constancy. This approach could inform advancements in other low-level computer vision tasks that have traditionally relied on generative models.

Future developments could include exploring deeper convolutional architectures that might leverage larger datasets for enhanced performance, much like advancements in object detection. Additionally, the application of CCC to real-time imaging systems, such as in-camera hardware, poses a promising area for practical implementations.

The findings represent a critical step toward optimizing accuracy and performance in color constancy, providing a foundation for subsequent research to refine discriminative approaches further and harness their potential in both theoretical and applied settings.