Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Design of High-Level Features for Photo Quality Assessment
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Efficient object category recognition using classemes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Towards computational models of the visual aesthetic appeal of consumer videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
High level describable attributes for predicting aesthetics and interestingness
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Action recognition by dense trajectories
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
AVA: A large-scale database for aesthetic visual analysis
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Automatic cinemagraphs for ranking beautiful scenes
Proceedings of the 20th ACM international conference on Multimedia
Where is the beauty?: retrieving appealing VideoScenes by learning Flickr-based graded judgments
Proceedings of the 20th ACM international conference on Multimedia
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The aesthetics of videos can be used as a useful clue to improve user satisfaction in many applications such as search and recommendation. In this paper, we demonstrate a computational approach to automatically evaluate the aesthetics of videos, with particular emphasis on identifying beautiful scenes. Using a standard classification pipeline, we analyze the effectiveness of a comprehensive set of features, ranging from low-level visual features, mid-level semantic attributes, to style descriptors. In addition, since there is limited public training data with manual labels of video aesthetics, we explore freely available resources with a simple assumption that people tend to share more aesthetically appealing works than unappealing ones. Specifically, we use images from DPChallenge and videos from Flickr as positive training data and the Dutch documentary videos as negative data, where the latter contain mostly old materials of low visual quality. Our extensive evaluations show that combining multiple features is helpful, and very promising results can be obtained using the noisy but annotation-free training data. On the NHK Multimedia Challenge dataset, we attain a Spearman's rank correlation coefficient of 0.41.