An evaluation criterion of saliency models for video seam carving
Modeling human attention has been arousing a lot of interest due to its numerous applications. The process that allows us to focus on some more important stimuli is defined as the “attention”. Seam carving is an approach to resize images or video sequences while preserving the semantic content. To define what is important, gradient was first used but due to limitations of this approach, saliency models, which are able to predict whether regions in the images attract human attention, are now used. Most of them are optimized to conform a ground truth like eye tracking but there is no way to know the efficiency of the saliency models applied in a specific application like in seam carving. In this paper, we propose a criterion, based on the quantity of geometric deformation and the image's reduction, which evaluate the quality of a resizing by seam carving. This criterion is applied on evaluation of image (SCES) or video (SCED) resizing. We validate our criterion with subjective evaluation and used it to rank state of the art saliency models for seam carving. Evaluation of the image by SCES gives a Spearman correlation of -0.92196 and a Pearson correlation of -0.8812. For the video, the final SCED gives a Spearman correlation of -0.81351 and a Pearson correlation of -0.80581.