In this paper, an aesthetic modeling method for scenic photographs is proposed. A bottom-up approach is developed to construct an aesthetic library with bag-of-aestheticspreserving features instead of top-down methods that implement the heuristic guidelines (rule-specific features) listed in the photography literature, which is employed in previous works. The proposed method can cover both implicit and explicit aesthetic features with a learning process. The experimental results show that the proposed features in the library (92.06% in accuracy) outperform the state-of-theart rule-specific features (83.63% in accuracy) significantly in the aesthetic quality assessment for scenic photos, and the rule-specific features are also proved to be encompassed by the proposed features. Meanwhile, it is observed from experiments that the features extracted for contrast information are more effective than those for absolute information, which is consistent with the properties of human visual systems.
With the explosive growth of camera devices, people can freely take photos to capture moments of life, especially the ones accompanied with friends and family. Therefore, a better solution to organize the increasing number of personal or group photos is highly required. In this paper, we propose a novel way to search for face images according facial attributes and face similarity of the target persons. To better match the face layout in mind, our system allows the user to graphically specify the face positions and sizes on a query "canvas," where each attribute or identity is defined as an "icon" for easier representation. Moreover, we provide aesthetics filtering to enhance visual experience by removing candidates of poor photographic qualities. The scenario has been realized on a touch device with an intuitive user interface. With the proposed block-based indexing approach, we can achieve near real-time retrieval (0.1 second on average) in a large-scale dataset (more than 200k faces in Flickr images).
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