Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
ACM SIGGRAPH 2006 Papers
Professional Techniques for Black & White Digital Photography
Professional Techniques for Black & White Digital Photography
Photo and Video Quality Evaluation: Focusing on the Subject
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
The role of tags and image aesthetics in social image search
WSM '09 Proceedings of the first SIGMM workshop on Social media
Saliency-enhanced image aesthetics class prediction
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A framework for photo-quality assessment and enhancement based on visual aesthetics
Proceedings of the international conference on Multimedia
Supporting personal photo storytelling for social albums
Proceedings of the international conference on Multimedia
Towards computational models of the visual aesthetic appeal of consumer videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Evaluating visual aesthetics in photographic portraiture
CAe '12 Proceedings of the Eighth Annual Symposium on Computational Aesthetics in Graphics, Visualization, and Imaging
Enhancing semantic features with compositional analysis for scene recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Semantic indexing and computational aesthetics: interactions, bridgesand boundaries
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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We present a novel data-driven category-based approach to automatically assess the aesthetic appeal of photographs. In order to tackle this problem, a novel set of image segmentation methods based on feature contrast are introduced, such that luminance , sharpness , saliency , color chroma , and a measure of region relevance are computed to generate different image partitions. Image aesthetic features are computed on these regions (e.g. sharpness , colorfulness , and a novel set of light exposure features). In addition, color harmony , image simplicity , and a novel set of image composition features are measured on the overall image. Support Vector Regression models are generated for each of 7 popular image categories: animals , architecture , cityscape , floral , landscape , portraiture and seascapes . These models are analyzed to understand which features have greater influence in each of those categories, and how they perform with respect to a generic state of the art model.