Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search.

Abstract

Many experiments have shown that the human visual system makes extensive use of contextual information for facilitating object search in natural scenes. However, the question of how to formally model contextual influences is still open. On the basis of a Bayesian framework, the authors present an original approach of attentional guidance by global scene context. The model comprises 2 parallel pathways; one pathway computes local features (saliency) and the other computes global (scenecentered) features. The contextual guidance model of attention combines bottom-up saliency, scene context, and top-down mechanisms at an early stage of visual processing and predicts the image regions likely to be fixated by human observers performing natural search tasks in real-world scenes.Keywords: eye movements, visual search, context, global feature, Bayesian model According to feature-integration theory (Treisman & Gelade, 1980), the search for objects requires slow serial scanning because attention is necessary to integrate low-level features into single objects. Current computational models of visual attention based on saliency maps have been inspired by this approach, as it allows a simple and direct implementation of bottom-up attentional mechanisms that are not task specific. Computational models of image saliency (Itti, Koch, & Niebur, 1998;Koch & Ullman, 1985;Parkhurst, Law, & Niebur, 2002;Rosenholtz, 1999) provide some predictions about which regions are likely to attract observers' attention. These models work best in situations in which the image itself provides little semantic information and in which no specific task is driving the observer's exploration. In real-world images, the semantic content of the scene, the co-occurrence of objects, and task constraints have been shown to play a key role in modulating where attention and eye movements go

Scene gist categorization by pigeons.

“…Given that scene gist recognition occurs within a single eye fixation (Eckstein et al, 2006;Torralba et al, 2006), and that fixation durations are generally tied to the processing times required for various visual recognition tasks (Nuthmann, Smith, Engbert, & Henderson, 2010;Rayner, 1998), it seems a reasonable assumption that pigeons may both require longer stimulus durations to recognize gist and tend make longer fixation durations than humans, both by roughly an order of magnitude.…”

Attentional modulation of the carry over of eye-movements between tasks

“…These authors noted that traditional models of eye movements (e.g., Itti & Koch, 2000;Torralba, Oliva, Castelhano & Henderson, 2006) fail to account for the influence of a preceding, but unrelated task when the information is not beneficial to the secondary task (i.e., exposure to a different scene or situation). As a result, the mechanisms that underlie this negative carry over effect are poorly understood.…”

Experience-Guided Search: A Theory of Attentional Control

“…Torralba et al (2006) and Zhang and Cottrell (submitted) have also suggested that saliency should reflect target probability, although they propose approaches to computing the target probability very different from ours. Our approach is to compute the target probability using statistics obtained from recent experience performing the task.…”