INTEL-TUT Dataset for Camera Invariant Color Constancy Research
This dataset addresses the need for robust color constancy algorithms that perform consistently across different cameras, though it is incremental as it builds on existing data collection efforts.
The authors introduced the INTEL-TUT dataset for camera invariant color constancy research, providing images of scenes captured by multiple cameras under various illuminations, and established a baseline using a convolutional neural network algorithm.
In this paper, we provide a novel dataset designed for camera invariant color constancy research. Camera invariance corresponds to the robustness of an algorithm's performance when run on images of the same scene taken by different cameras. Accordingly, images in the database correspond to several lab and field scenes each of which are captured by three different cameras with minimal registration errors. The lab scenes are also captured under five different illuminations. The spectral responses of cameras and the spectral power distributions of the lab light sources are also provided, as they may prove beneficial for training future algorithms to achieve color constancy. For a fair evaluation of future methods, we provide guidelines for supervised methods with indicated training, validation and testing partitions. Accordingly, we evaluate a recently proposed convolutional neural network based color constancy algorithm as a baseline for future research. As a side contribution, this dataset also includes images taken by a mobile camera with color shading corrected and uncorrected results. This allows research on the effect of color shading as well.