Making large-scale support vector machine learning practical
Advances in kernel methods
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
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
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Recovering Surface Layout from an Image
International Journal of Computer Vision
Learning the consensus on visual quality for next-generation image management
Proceedings of the 15th international conference on Multimedia
Automatic Estimation and Removal of Noise from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data-driven enhancement of facial attractiveness
ACM SIGGRAPH 2008 papers
Photo and Video Quality Evaluation: Focusing on the Subject
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
A Hybrid Image Quality Measure for Automatic Image Quality Assessment
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Photo assessment based on computational visual attention model
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Perceptual quality assessment based on visual attention analysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Sensation-based photo cropping
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Bayesian fusion of camera metadata cues in semantic scene classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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 based on a degradation model
IEEE Transactions on Image Processing
Snap & play: auto-generate personalized find-the-difference mobile game
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Scenic photo quality assessment with bag of aesthetics-preserving features
MM '11 Proceedings of the 19th ACM international conference on Multimedia
The role of attractiveness in web image search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Prediction of the inter-observer visual congruency (IOVC) and application to image ranking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
OSCAR: On-Site Composition and Aesthetics Feedback Through Exemplars for Photographers
International Journal of Computer Vision
Towards category-based aesthetic models of photographs
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Evaluating visual aesthetics in photographic portraiture
CAe '12 Proceedings of the Eighth Annual Symposium on Computational Aesthetics in Graphics, Visualization, and Imaging
Enhancing visual dominance by semantics-preserving image recomposition
Proceedings of the 20th ACM international conference on Multimedia
Automatic cinemagraphs for ranking beautiful scenes
Proceedings of the 20th ACM international conference on Multimedia
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
How self-similar are artworks at different levels of spatial resolution?
Proceedings of the Symposium on Computational Aesthetics
Estimating beauty ratings of videos using supervoxels
Proceedings of the 21st ACM international conference on Multimedia
CAD/Graphics 2013: Efficient view manipulation for cuboid-structured images
Computers and Graphics
Where should I stand? Learning based human position recommendation for mobile photographing
Multimedia Tools and Applications
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We present an interactive application that enables users to improve the visual aesthetics of their digital photographs using spatial recomposition. Unlike earlier work that focuses either on photo quality assessment or interactive tools for photo editing, we enable the user to make informed decisions about improving the composition of a photograph and to implement them in a single framework. Specifically, the user interactively selects a foreground object and the system presents recommendations for where it can be moved in a manner that optimizes a learned aesthetic metric while obeying semantic constraints. For photographic compositions that lack a distinct foreground object, our tool provides the user with cropping or expanding recommendations that improve its aesthetic quality. We learn a support vector regression model for capturing image aesthetics from user data and seek to optimize this metric during recomposition. Rather than prescribing a fully-automated solution, we allow user-guided object segmentation and inpainting to ensure that the final photograph matches the user's criteria. Our approach achieves 86% accuracy in predicting the attractiveness of unrated images, when compared to their respective human rankings. Additionally, 73% of the images recomposited using our tool are ranked more attractive than their original counterparts by human raters.