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摘要
The goal of photo aesthetics assessment is to build a computational model which can estimate the aesthetics quality of digital images with respect to human's perception. As one of the most important features that determine the degree of image's aesthetics quality, color harmony has gained increasing attentions. To overcome the problems of most classical color harmony models, which are heavily relied on heuristic rules and ignore the semantic information of images, we propose a statistical learning framework in this paper to train a color harmony model from a large number of natural images. In this framework, the semantic label information, which indicates the content of each image, along with the visual features is used to facilitate the latent Dirichlet allocation (LDA) training. Then, the degree of color harmony can be estimated by using supervised/unsupervised models, which is applied to indicate the photo's aesthetics score. By using the proposed color harmony model, we attempt to uncover the underlying principles that generate pleasing color combinations based on natural images. The experimental results show that the proposed approach outperforms the conventional heuristic color harmony models for image aesthetics assessment.
