Although many visual attention models have been proposed, very few saliency models investigated the impact of audio information. To develop audio-visual attention models, researchers need to have a ground truth of eye movements recorded while exploring complex natural scenes in different audio conditions. They also need tools to compare eye movements and gaze patterns between these different audio conditions. This paper describes a toolbox that answer these needs by proposing a new eye-tracking dataset and its associated analysis ToolBox that contains common metrics to analysis eye movements. Our eye-tracking dataset contains the eye positions gathered during four eye-tracking experiments. A total of 176 observers were recorded while exploring 148 videos (mean duration = 22 s) split between different audio conditions (with or without sound) and visual categories (moving objects, landscapes and faces). Our ToolBox allows to visualize the temporal evolution of different metrics computed from the recorded eye positions. Both dataset and ToolBox are freely available to help design and assess visual saliency models for audiovisual dynamic stimuli.

Saliency models provide heatmaps highlighting the probability of each pixel to attract human gaze. To define image's important regions, features maps are extracted. The rarity, surprise or contrast are computed leading to conspicuity maps, showing important regions of each feature map. The final saliency map is obtained by merging these maps. The fusion process is usually a linear combination of the maps where the coefficients show their importance. We propose a novel generic fusion mechanism based on 1) using a rarity-based attention module and 2) using neural networks to achieve the fusion. The first layer of the NN merges the weighted feature maps into a saliency map. The second layer takes into account the spatial information. The approach is compared to 8 models using 4 different comparison metrics on open state-of-the-art databases.

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.

Visual saliency has been an increasingly active research area in the last ten years with dozens of saliency models recently published. Nowadays, one of the big challenges in the field is to find a way to fairly evaluate all of these models. In this paper, on human eye fixations, we compare the ranking of 12 state-of-the art saliency models using 12 similarity metrics. The comparison is done on Jian Li's database containing several hundreds of natural images. Based on Kendall concordance coefficient, it is shown that some of the metrics are strongly correlated leading to a redundancy in the performance metrics reported in the available benchmarks. On the other hand, other metrics provide a more diverse picture of models' overall performance. As a recommendation, three similarity metrics should be used to obtain a complete point of view of saliency model performance.

In this paper, a new spatio-temporal saliency model is presented. Based on the idea that both spatial and temporal features are needed to determine the saliency of a video, this model builds upon the fact that locally contrasted and globally rare features are salient. The features used in the model are both spatial (color and orientations) and temporal (motion amplitude and direction) at several scales. To be more robust to moving camera a module computes the global motion and to be more consistent in time, the saliency maps are combined together after a temporal filtering. The model is evaluated on a dataset of 24 videos split into 5 categories (Abnormal, Surveillance, Crowds, Moving camera, and Noisy). This model achieves better performance when compared to several state-of-the-art saliency models.

In this paper, four new approaches of global context recognition algorithms (gist) are introduced. They are able to automatically distinguish context differences like buildings, coast, home (indoor), mountain or streets. All proposed models are biologically plausible and are able to deal with both color and gray-level images. They use Gabor or Log-Gabor filters to extract features that better mimic human visual perception. Those features are then classified using a Mahalanobis space (when a subset of features is extracted) or in a high-dimensional Gaussian space (when all features are taken into account) with Support Vector Machines (SVM). The proposed models are compared to a standard state of the art gist model to proof their efficiency.

In this paper, a new bottom-up visual saliency model is proposed. Based on the idea that locally contrasted and globally rare features are salient, this model will be called “RARE” in the following sections. It uses a sequential bottom-up features extraction where first low-level features as luminance and chrominance are computed and from those results medium-level features as image orientations are extracted. A qualitative and a quantitative comparison are achieved on a 120 images dataset. The RARE algorithm powerfully predicts human fixations compared with most of the freely available saliency models.

This paper deals with the selection of relevant motion from multi-object movement. The proposed method is based on a multi-scale approach using features extracted from optical flow and global rarity quantification to compute bottom-up saliency maps. It shows good results from four objects to dense crowds with increasing performance. The results are convincing on synthetic videos, simple real video movements, a pedestrian database and they seem promising on very complex videos with dense crowds. This algorithm only uses motion features (direction and speed) but can be easily generalized to other dynamic or static features. Video surveillance, social signal processing and, in general, higher level scene understanding can benefit from this method.