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Conference Papers Year : 2011

Prediction of the Inter-Observer Visual Congruency (IOVC) and Application to Image Ranking

Abstract

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.
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Dates and versions

inria-00628077 , version 1 (30-09-2011)
inria-00628077 , version 2 (20-04-2012)

Identifiers

  • HAL Id : inria-00628077 , version 2

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Olivier Le Meur, Thierry Baccino, Aline Roumy. Prediction of the Inter-Observer Visual Congruency (IOVC) and Application to Image Ranking. ACM Multimedia, Nov 2011, Phoneix, United States. ⟨inria-00628077v2⟩
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