A Hybrid Strategy for Illuminant Estimation Targeting Hard Images

Illumination estimation is a well-studied topic in computer vision. Early work reported performance on benchmark datasets using simple statistical aggregates such as mean or median error. Recently, it has become accepted to report a wider range of statistics, e.g. top 25%, mean, and bottom 25% performance. While these additional statistics are more informative, their relationship across different methods is unclear. In this paper, we analyse the results of a number of methods to see if there exist ‘hard’ images that are challenging for multiple methods. Our findings indicate that there are certain images that are difficult for fast statistical-based methods, but that can be handled with more complex learning-based approaches at a significant cost in time-complexity. This has led us to design a hybrid method that first classifies an image as ‘hard’ or ‘easy’ and then uses the slower method when needed, thus providing a balance between time-complexity and performance. In addition, we have identified dataset images that almost no method is able to process. We argue, however, that these images have problems with how the ground truth is established and recommend their removal from future performance evaluation

Color Constancy Using CNNs

In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max pooling, one fully connected layer and three output nodes. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating scene illumination. This approach achieves state-of-the-art performance on a standard dataset of RAW images. Preliminary experiments on images with spatially varying illumination demonstrate the stability of the local illuminant estimation ability of our CNN.Comment: Accepted at DeepVision: Deep Learning in Computer Vision 2015 (CVPR 2015 workshop