SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
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
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Informational Aesthetics Measures
IEEE Computer Graphics and Applications
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
How self-similar are artworks at different levels of spatial resolution?
Proceedings of the Symposium on Computational Aesthetics
Hi-index | 0.00 |
Previous research in computational aesthetics has led to the identification of multiple image features that, in combination, can be related to the aesthetic quality of images, such as photographs. Moreover, it has been shown that aesthetic artworks possess specific higher-order statistical properties, such as a scale-invariant Fourier spectrum, that can be linked to coding mechanisms in the human visual system. In the present work, we derive novel measures based on a PHOG representation of images for image properties that have been studied in the context of the aesthetic assessment of images previously. We demonstrate that a large dataset of colored aesthetic paintings of Western provenance is characterized by a specific combination of the PHOG-derived aesthetic measures (high self-similarity, moderate complexity and low anisotropy). In this combination, the artworks differ significantly from seven other datasets of photographs that depict various types of natural and man-made scenes, patterns and objects. To the best of our knowledge, this is the first time that these features have been derived and evaluated on a large dataset of different image categories.