Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A user attention model for video summarization
Proceedings of the tenth ACM international conference on 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
Visual attention detection in video sequences using spatiotemporal cues
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Photo and Video Quality Evaluation: Focusing on the Subject
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Global contrast based salient region detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Content-based photo quality assessment
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Simplicity refers to one of the most important photography composition rules. Simplicity states that simplifying the image background can draw viewers' attention to the subject of interest in a photograph and help them better comprehend and appreciate it. Understanding whether a photo respects photography rules or not facilitates photo quality assessment. In this paper, we present a method to automatically detect whether a photo is composed according to the rule of simplicity. We design features according to the definition, implementation and effect of the rule. First, we make use of saliency analysis to infer the subject of interest in a photo and measure its compactness. Second, we segment an image into background and foreground and measure the homogeneity within the background as another feature. Third, when looking at an image created with the rule of simplicity, different viewers tend to agree on what the subject of interest is in this photo. We accordingly measure the consistency among various saliency detection results as a feature. We experiment with these features in a range of machine learning methods. Our experiments show that our methods, together with these features, provide an encouraging result in detecting the rule of simplicity in a photo.