INTEL-TAU: A Color Constancy Dataset
This provides a new dataset for researchers in computer vision to evaluate illumination estimation techniques, but it is incremental as it primarily offers more data rather than a novel method.
The authors introduced INTEL-TAU, a large dataset of 7022 high-resolution images for illumination estimation, making it the largest available, and benchmarked several color constancy approaches on it.
In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research. The variety of scenes captured using three different camera models, namely Canon 5DSR, Nikon D810, and Sony IMX135, makes the dataset appropriate for evaluating the camera and scene invariance of the different illumination estimation techniques. Privacy masking is done for sensitive information, e.g., faces. Thus, the dataset is coherent with the new General Data Protection Regulation (GDPR). Furthermore, the effect of color shading for mobile images can be evaluated with INTEL-TAU dataset, as both corrected and uncorrected versions of the raw data are provided. Furthermore, this paper benchmarks several color constancy approaches on the proposed dataset.