Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention

A critical function in both machine vision and biological vision systems is attentional selection of scene regions worthy of further analysis by higher-level processes such as object recognition. Here we present the first model of spatial attention that (1) can be applied to arbitrary static and dynamic image sequences with interactive tasks and (2) combines a general computational implementation of both bottom-up (BU) saliency and dynamic top-down (TD) task relevance; the claimed novelty lies in the combination of these elements and in the fully computational nature of the model. The BU component computes a saliency map from 12 low-level multi-scale visual features. The TD component computes a low-level signature of the entire image, and learns to associate different classes of signatures with the different gaze patterns recorded from human subjects performing a task of interest. We measured the ability of this model to predict the eye movements of people playing contemporary video g...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Robert J. Peters, Laurent Itti
• Keywords: Bu Component, Bu Model, Computer Vision, CVPR 2007, Td Component, Td Factors, Td Model