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The spread of social networking services has created an increasing demand forselecting, editing, and generating impressive images. This trend increases theimportance of evaluating image aesthetics as a complementary function ofautomatic image processing. We propose a multi-patch method, named MPA-Net(Multi-Patch Aggregation Network), to predict image aesthetics scores bymaintaining the original aspect ratios of contents in the images. Through anexperiment involving the large-scale AVA dataset, which contains 250,000images, we show that the effectiveness of the equal-interval multi-patchselection approach for aesthetics score prediction is significant compared tothe single-patch prediction and random patch selection approaches. For thisdataset, MPA-Net outperforms the neural image assessment algorithm, which wasregarded as a baseline method. In particular, MPA-Net yields a 0.073 (11.5%)higher linear correlation coefficient (LCC) of aesthetics scores and a 0.088(14.4%) higher Spearman's rank correlation coefficient (SRCC). MPA-Net alsoreduces the mean square error (MSE) by 0.0115 (4.18%) and achieves results forthe LCC and SRCC that are comparable to those of the state-of-the-artcontinuous aesthetics score prediction methods. Most notably, MPA-Net yields asignificant lower MSE especially for images with aspect ratios far from 1.0,indicating that MPA-Net is useful for a wide range of image aspect ratios.MPA-Net uses only images and does not require external information during thetraining nor prediction stages. Therefore, MPA-Net has great potential forapplications aside from aesthetics score prediction such as other humansubjectivity prediction.

Image Aesthetics Prediction Using Multiple Patches Preserving the Original Aspect Ratio of Contents