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
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ACM SIGGRAPH 2007 papers
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International Journal of Computer Vision
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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
Large-scale visual sentiment ontology and detectors using adjective noun pairs
Proceedings of the 21st ACM international conference on Multimedia
Towards a comprehensive computational model foraesthetic assessment of videos
Proceedings of the 21st ACM international conference on Multimedia
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This article presents an interactive application that enables users to improve the visual aesthetics of their digital photographs using several novel spatial recompositing techniques. This work differs from earlier efforts in two important aspects: (1) it focuses on both photo quality assessment and improvement in an integrated fashion, (2) it enables the user to make informed decisions about improving the composition of a photograph. The tool facilitates interactive selection of one or more than one foreground objects present in a given composition, and the system presents recommendations for where it can be relocated in a manner that optimizes a learned aesthetic metric while obeying semantic constraints. For photographic compositions that lack a distinct foreground object, the tool provides the user with crop or expansion recommendations that improve the aesthetic appeal by equalizing the distribution of visual weights between semantically different regions. The recomposition techniques presented in the article emphasize learning support vector regression models that capture visual aesthetics from user data and seek to optimize this metric iteratively to increase the image appeal. The tool demonstrates promising aesthetic assessment and enhancement results on variety of images and provides insightful directions towards future research.