Many successful models for predicting attention in a scene involve three main steps:
convolution with a set of filters, a center-surround mechanism and spatial pooling to …

In the past decade, a large number of computational models of visual saliency have been
proposed. Recently a number of comprehensive benchmark studies have been presented …

The predictions of 13 computational bottom-up saliency models and a newly introduced
Multiscale Contrast Conspicuity (MCC) metric are compared with human visual conspicuity …

Inspired by the primate visual system, computational saliency models decompose visual
input into a set of feature maps across spatial scales in a number of pre-specified channels …

We propose a definition of saliency by considering what the visual system is trying to
optimize when directing attention. The resulting model is a Bayesian framework from which …

Saliency modeling has been an active research area in computer vision for about two
decades. Existing state of the art models perform very well in predicting where people look …

Eye tracking has become the de facto standard measure of visual attention in tasks that
range from free viewing to complex daily activities. In particular, saliency models are often …

This paper presents a novel approach to visual saliency that relies on a contextually
adapted representation produced through adaptive whitening of color and scale features …

Visual attention is the ability of the human vision system to detect salient parts of the scene,
on which higher vision tasks, such as recognition, can focus. In human vision, it is believed …

This paper addresses the bottom-up influence of local image information on human eye
movements. Most existing computational models use a set of biologically plausible linear …