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Deep Aesthetic Learning Estimating Photo Aesthetic Rating Using Deep Convolutional Neural Network

Deep Learning has been widely applied in several computer vision applications such as item recognition and image retrieval. In addition to identifying what a image contains, it is also a interesting problem to explore about whether the image is appealing or the composition of the items are highly rated aesthetically. There are multiple studies using hand-designed or datadriven model to quantify the metrics regarding to photo quality such as color distribution, composition, sharpness etc. Aesthetic rating is such a complicated and unknown function of human beings’ psychological responses to the pixels that deep networks are potentially better tools to achieve similar modeling and predictions. Existing large aesthetic rating data-set also enable the training good deep networks. In this work, I developed a convolution neural network dedicated for learning to model and predict photo ratings from pixels. Novel data augmentation scheme and further fine tuning regression from the learned features are the two main parts of the contributions in this work. Results demonstrate better prediction performance compared with existing solutions using either conventional engineered features or deep networks. In addition, the trained network is able to out-perform human beings in estimating the aesthetic rating of images. The resulting performance are summarized and more details of network design and tuning are further discussed. The trained networks are further quantified which also provides insights to the understanding of human being’s appreciation to art and beauty.