Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Where Does Computational Media Aesthetics Fit?
IEEE MultiMedia
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
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personalized photograph ranking and selection system
Proceedings of the international conference on Multimedia
A framework for photo-quality assessment and enhancement based on visual aesthetics
Proceedings of the international conference on Multimedia
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Answering search queries with CrowdSearcher
Proceedings of the 21st international conference on World Wide Web
What makes an image memorable?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis
Proceedings of the 21st ACM international conference on Multimedia
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The John Ruskin's 19th century adage suggests that personal taste is not merely an absolute set of aesthetic principles valid for everyone: actually, it is a process of interpretation which have also roots in one's life experiences. This aspect represents nowadays a major problem for inferring automatically the quality of a picture. In this paper, instead of trying to solve this age-old problem, we consider an intriguing, orthogonal direction, aimed at discovering how different are the personal tastes. Given a set of preferred images of a user, obtained from Flickr, we extract a pool of low- and high-level features; LASSO regression is then exploited to learn the most discriminative ones, considering a group of 200 random Flickr users. Such aspects can be easily recovered, allowing to understand what is the "what we like" which distinguish us from the others. We then perform multi-class classification, where a test sample is a set of preferred pictures of an unknown user, and the classes are all the users. The results are surprising: given only 1 image as test, we can match the user preferences definitely more than the chance, and with 20 images we reach an nAUC of 91%, considering the cumulative matching characteristic curve. Extensive experiments promote our approach, suggesting new intriguing perspectives in the study of computational aesthetics.