The nature of statistical learning theory
The nature of statistical learning theory
Detection and removal of lighting & shaking artifacts in home videos
Proceedings of the tenth ACM international conference on Multimedia
ACM SIGGRAPH 2006 Papers
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
Photo and Video Quality Evaluation: Focusing on the Subject
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Where are focused places of a photo?
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Classification of digital photos taken by photographers or home users
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Visual quality assessment algorithms: what does the future hold?
Multimedia Tools and Applications
Color compatibility from large datasets
ACM SIGGRAPH 2011 papers
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Candid portrait selection from video
Proceedings of the 2011 SIGGRAPH Asia Conference
Towards category-based aesthetic models of photographs
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
MoViMash: online mobile video mashup
Proceedings of the 20th ACM international conference on Multimedia
Towards a comprehensive computational model foraesthetic assessment of videos
Proceedings of the 21st ACM international conference on Multimedia
Beauty is here: evaluating aesthetics in videos using multimodal features and free training data
Proceedings of the 21st ACM international conference on Multimedia
Estimating beauty ratings of videos using supervoxels
Proceedings of the 21st ACM international conference on Multimedia
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In this paper, we tackle the problem of characterizing the aesthetic appeal of consumer videos and automatically classifying them into high or low aesthetic appeal. First, we conduct a controlled user study to collect ratings on the aesthetic value of 160 consumer videos. Next, we propose and evaluate a set of low level features that are combined in a hierarchical way in order to model the aesthetic appeal of consumer videos. After selecting the 7 most discriminative features, we successfully classify aesthetically appealing vs. aesthetically unappealing videos with a 73% classification accuracy using a support vector machine.