In this paper, the framework for a real-time view recommendation system is proposed. The proposed system comprises two parts: offline aesthetic modeling stage and efficient online aesthetic view finding process. A preference-aware aesthetic model is proposed to suggest views according to varied user-favorite photographic styles, where a bottom-up approach is developed to construct an aesthetic feature library with bag-of-aesthetics-preserving features instead of top-down methods that implement the heuristic guidelines (rule-specific features) listed in photography literatures, which is employed in previous works. A collection of scenic photos is used as the test set; however, the proposed method can be employed to other types of photo collection according to different application scenarios. The proposed model can cover both implicit and explicit aesthetic features and can adapt to users' preferences with a learning process. In the second part, the learned model is employed in a view finder to help the user to locate the most aesthetic view while taking a photograph. The experimental results show that the proposed features in the library (92.06% in accuracy) outperform the state-of-the-art rule-specific features (83.63% in accuracy) significantly in the photo aesthetic quality classification task, 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. Furthermore, the user studies for the view recommendation task confirm that the suggested views are consistent with users' preferences (81.25% agreements).

In this paper, a new video retargeting method based on temporal information and seam carving is presented. Two video energy functions, motion weight prediction and pixel-based optimization, are proposed to take the temporal information into account and make dynamic programming available during the process of retargeting. The motion weight prediction exploits both the block-based motion estimation and Gaussian masks to predict the coarse location of seams in the current frame and reduce the search range of dynamic programming. The pixel-based optimization then utilizes the concept of pixel-based optical flow to explore better temporal relations between the current frame and previous frames in the reduced search range. The experimental results show that combining these two video energy functions as well as dynamic programming, the proposed method could achieve content-aware and temporal smoothing retargeting results with less computational complexity.

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).

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-aesthetics preserving 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-the-art 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.

A new image segmentation method is proposed to combine the edge information with the feature-space method, K-Means clustering. A procedure called seam processing, which is computationally efficient, is employed to search for horizontal and vertical seams that contain edge information. By transforming the spatial coordinates based on the seam detection results, the edge information can be added to the feature vectors, which are the inputs of K-Means algorithm. The experiments show that the proposed method can achieve edge-adaptive segmentation results, which can not be obtained using traditional methods based on K-Means clustering.