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Nowadays, social media has become a popular platform for the public to sharephotos. To make photos more visually appealing, users usually apply filters ontheir photos without domain knowledge. However, due to the growing number offilter types, it becomes a major issue for users to choose the best filtertype. For this purpose, filter recommendation for photo aesthetics takes animportant role in image quality ranking problems. In these years, several workshave declared that Convolutional Neural Networks (CNNs) outperform traditionalmethods in image aesthetic categorization, which classifies images into high orlow quality. Most of them do not consider the effect on filtered images; hence,we propose a novel image aesthetic learning for filter recommendation. Insteadof binarizing image quality, we adjust the state-of-the-art CNN architecturesand design a pairwise loss function to learn the embedded aesthetic responsesin hidden layers for filtered images. Based on our pilot study, we observeimage categories (e.g., portrait, landscape, food) will affect user preferenceon filter selection. We further integrate category classification into ourproposed aesthetic-oriented models. To the best of our knowledge, there is nopublic dataset for aesthetic judgment with filtered images. We create a newdataset called Filter Aesthetic Comparison Dataset (FACD). It contains 28,160filtered images based on the AVA dataset and 42,240 reliable image pairs withaesthetic annotations using Amazon Mechanical Turk. It is the first datasetcontaining filtered images and user preference labels. We conduct experimentson the collected FACD for filter recommendation, and the results show that ourproposed category-aware aesthetic learning outperforms aesthetic classificationmethods (e.g., 12% relative improvement).

Photo Filter Recommendation by Category-Aware Aesthetic Learning