  Thumbnails play such an important role in online videos. As the most
representative snapshot, they capture the essence of a video and provide the
first impression to the viewers; ultimately, a great thumbnail makes a video
more attractive to click and watch. We present an automatic thumbnail selection
system that exploits two important characteristics commonly associated with
meaningful and attractive thumbnails: high relevance to video content and
superior visual aesthetic quality. Our system selects attractive thumbnails by
analyzing various visual quality and aesthetic metrics of video frames, and
performs a clustering analysis to determine the relevance to video content,
thus making the resulting thumbnails more representative of the video. On the
task of predicting thumbnails chosen by professional video editors, we
demonstrate the effectiveness of our system against six baseline methods, using
a real-world dataset of 1,118 videos collected from Yahoo Screen. In addition,
we study what makes a frame a good thumbnail by analyzing the statistical
relationship between thumbnail frames and non-thumbnail frames in terms of
various image quality features. Our study suggests that the selection of a good
thumbnail is highly correlated with objective visual quality metrics, such as
the frame texture and sharpness, implying the possibility of building an
automatic thumbnail selection system based on visual aesthetics.
