A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational visual attention systems and their cognitive foundations: A survey
ACM Transactions on Applied Perception (TAP)
Context based object categorization: A critical survey
Computer Vision and Image Understanding
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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The mechanism in the brain that determines which part of the multitude of sensory data is currently of most interest is called selective attention. There are two kinds of attention cues, stimulus-driven bottom-up cues and goal-driven top-down cues determined by cognitive phenomena like knowledge, expectations, reward, and current goals. In this paper, we propose a Bayesian approach that explains the optimal integration of top-down cues and bottom-up cues. The top down cues include appearance feature, contexts, and locations of a target. The bottom up attention (saliency) is defined as the joint probability of the local feature and context at a location in the scene. The feature and context is organized in a pyramid structure. In this way, multiscale saliency is easily implemented. We demonstrate that the proposed visual saliency effectively predicts human gaze in free-viewing of natural scenes.