A Learning-to-Rank Approach for Image Color Enhancement

• Published in: 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 2987 - 2994
• Main Authors: Yan, Jianzhou, Lin, Stephen, Kang, Sing Bing, Tang, Xiaoou
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.06.2014
• Subjects: Automatic enhancement; Brightness; Color; Computer vision; Conferences; Decision trees; Feature extraction; Histograms; Human; Image color analysis; Image Color Enhancement; Image enhancement; Learning-to-Rank; Pattern recognition; Ranking; Training; Vectors
• ISSN: 1063-6919; 1063-6919; 2575-7075
• DOI: 10.1109/CVPR.2014.382

We present a machine-learned ranking approach for automatically enhancing the color of a photograph. Unlike previous techniques that train on pairs of images before and after adjustment by a human user, our method takes into account the intermediate steps taken in the enhancement process, which provide detailed information on the person's color preferences. To make use of this data, we formulate the color enhancement task as a learning-to-rank problem in which ordered pairs of images are used for training, and then various color enhancements of a novel input image can be evaluated from their corresponding rank values. From the parallels between the decision tree structures we use for ranking and the decisions made by a human during the editing process, we posit that breaking a full enhancement sequence into individual steps can facilitate training. Our experiments show that this approach compares well to existing methods for automatic color enhancement.