Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
The nature of mathematical modeling
The nature of mathematical modeling
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth Estimation from Image Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2006 Papers
ACM SIGGRAPH 2006 Papers
Photo and Video Quality Evaluation: Focusing on the Subject
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Photo assessment based on computational visual attention model
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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
Hi-index | 0.00 |
This paper proposes an automatic method for predicting the inter-observer visual congruency (IOVC). The IOVC reflects the congruence or the variability among different subjects looking at the same image. Predicting this congruence is of interest for image processing applications where the visual perception of a picture matters such as website design, advertisement, etc. This paper makes several new contributions. First, a computational model of the IOVC is proposed. This new model is a mixture of low-level visual features extracted from the input picture where model's parameters are learned by using a large eye-tracking database. Once the parameters have been learned, it can be used for any new picture. Second, regarding low-level visual feature extraction, we propose a new scheme to compute the depth of field of a picture. Finally, once the training and the feature extraction have been carried out, a score ranging from 0 (minimal congruency) to 1 (maximal congruency) is computed. A value of 1 indicates that observers would focus on the same locations and suggests that the picture presents strong locations of interest. A second database of eye movements is used to assess the performance of the proposed model. Results show that our IOVC criterion outperforms the Feature Congestion measure \cite{Rosenholtz2007}. To illustrate the interest of the proposed model, we have used it to automatically rank personalized photograph.