Photo Composition with RealTime Rating Taking a photo has become a part of our daily life. With the powerfulness and convenience of a smartphone, capturing what we see may have never been easier. However, taking good photos may not be always easy or intuitive for everyone. As numerous studies have shown that photo composition plays a very important role in making a good photo, in this study, we, therefore, propose to develop a phototaking app to give a realtime suggestion through a scoring mechanism to guide a user to take a good photo. Due to the concern of realtime performance, only eight commonly used composition rules are adopted in our system, and several detailed evaluations have been conducted to prove the effectiveness of our system.

SemiAutomatic Photo Clustering with Distance Metric Learning Photo clustering has been widely explored in many applications such as album management. But automatic clustering can hardly achieve satisfying performance due to the large variety of photosu0027 content. This paper proposes a semiautomatic photo clustering scheme that attempts to improve clustering performance with usersu0027 interactions. Users can adjust the results of automatic clustering, and a set of constraints among photos are generated accordingly. A distance metric is then learned with these constraints and we can reimplement clustering with this metric. We conduct experiments on different photo albums, and experimental results have demonstrated that our approach is able to improve automatic photo clustering results, and it is better than pure manual adjustment approach by exploring distance metric learning.