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Automatic prediction of photo aesthetic quality is useful for many practical purposes. Current computational approaches typically solved this problem by assigning a categorical label (good or bad) to a photo. However, due to the subjectivity and complexity of humans aesthetic judgments, only a categorical label is insufficient to represent humans perceived aesthetic quality of a photo. This paper...
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