A Learning-to-Rank Approach for Image Color Enhancement
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員

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摘要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.
著者Yan JZ, Lin S, Kang SB, Tang X
會議名稱27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
會議開始日23.06.2014
會議完結日28.06.2014
會議地點Columbus
會議國家/地區美國
詳細描述organized by Sven Dickinson, Matthew Turk, Dimitri Metaxas,
出版年份2014
月份1
日期1
出版社IEEE
頁次2987 - 2994
電子國際標準書號978-1-4799-5117-8
國際標準期刊號1063-6919
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence

上次更新時間 2021-11-05 於 00:36