Recently, large breakthroughs have been observed in saliency modeling. The top scores on saliency benchmarks have become dominated by neural network models of saliency, and some evaluation scores have begun to saturate. Large jumps in performance relative to previous models can be found across datasets, image types, and evaluation metrics. Have saliency models begun to converge on human perform...

Theories and models on saliency that predict where people look at focus on regular-density scenes. A crowded scene is characterized by the cooccurrence of a relatively large number of regions/objects that would have stood out if in a regular scene, and what drives attention in crowd can be significantly different from the conclusions in the regular setting. This work presents a first focused st...

In this paper, we propose a saliency detection model for RGB-D images based on the contrasting features of colour and depth with a generative mixture model. The depth feature map is extracted based on superpixel contrast computation with spatial priors. We model the depth saliency map by approximating the density of depth-based contrast features using a Gaussian distribution. Similar to the dep...

According to so-called saliency-based attention models, attention during free viewing of visual scenes is particularly allocated to physically salient image regions. In the present study, we assumed that social features in complex naturalistic scenes would be processed preferentially irrespective of their physical saliency. Therefore, we expected worse prediction of gazing behavior by saliency-...

Mesh saliency was introduced and joined the community of computer graphics ten years ago, which can benefit various applications, for instance, mesh reduction, mesh segmentation, self-similarity matching, scan integration, volume rendering, 3D printing, etc. Before, saliency detection had been successfully applied to image processing and pattern recognition to study how the world is perceptuall...

In this paper, a novel method is proposed to detect salient regions in images. To measure pixel-level saliency, joint spatial-color constraint is defined, i.e., spatial constraint (SC), color double-opponent (CD) constraint and similarity distribution (SD) constraint. The SC constraint is designed to produce global contrast with ability to distinguish the difference between ‘‘center and surroun...

There is a growing consensus that the brain computes value and saliency-like signals at the time of decision-making. Value signals are essential for making choices. Saliency signals are related to motivation, attention, and arousal. Unfortunately, an unequivocal characterization of the areas involved in these 2 distinct sets of processes is made difficult by the fact that, in most experiments, ...

A bottom-up or saliency driven attention allows the brain to detect nonspecific conspicuous targets in cluttered scenes before fully processing and recognizing the targets. Here, a novel biologically plausible auditory saliency map is presented to model such saliency based auditory attention. Multi-scale auditory features are extracted based on the processing stages in the central auditory syst...

Since the early 2000s, computational visual saliency has been a very active research area. Each year, more and more new models are published in the main computer vision conferences. Nowadays, one of the big challenges is to find a way to fairly evaluate all of these models. In this paper, a new framework is proposed to assess models of visual saliency. This evaluation is divided into three expe...

In this paper, a new method for saliency detection is proposed. Based on the defined features of the salient object, we solve the problem of saliency detection from three aspects. Firstly, from the view of global information, we partition the image into two clusters, namely, salient component and background component by employing Principal Component Analysis (PCA) and k-means clustering. Second...