This paper proposes an automatic method for predicting the inter-observer visual congruency (IOVC). The IOVC re- flects the congruence or the variability among different sub- jects looking at the same image. Predicting this congru- ence 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 pa- rameters 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 fea- ture extraction have been carried out, a score ranging from 0 (minimal congruency) to 1 (maximal congruency) is com- puted. 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 [33]. To illustrate the interest of the proposed model, we have used it to automatically rank personalized photograph.