A Learning-to-Rank Approach for Image Color Enhancement
CVPR(2014)
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.
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关键词
decision tree structures,learning (artificial intelligence),machine-learned ranking approach,automatic enhancement,learning-to-rank,image color enhancement, learning-to-rank, automatic enhancement,color photograph,image color enhancement,learning-to-rank approach,image enhancement,decision trees,image colour analysis,histograms,vectors,learning to rank,feature extraction,brightness,learning artificial intelligence
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