Visit: an efficient computational model of human visual attention
Visit: an efficient computational model of human visual attention
Toward a computational model of visual attention
Early vision and beyond
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data- and Model-Driven Gaze Control for an Active-Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing Visual Attention from Scene Depth
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Real-time visual attention on a massively parallel SIMD architecture
Real-Time Imaging
A model of dynamic visual attention for object tracking in natural image sequences
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
MAPS: multiscale attention-based presegmentation of color images
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Salient region detection by modeling distributions of color and orientation
IEEE Transactions on Multimedia
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Visual attention is the ability of a vision system, be it biological or artificial, to rapidly detect potentially relevant parts of a visual scene, on which higher level vision tasks, such as object recognition, can focus. The saliency-based model of visual attention represents one of tile main attempts to simulate this visual mechanism on computers. Though biologically inspired, this model has only been partially assessed in comparison with human behavior. Our methodology consists in comparing the computational saliency map with human eye movement patterns. This paper presents an in-depth analysis of the model by assessing the contribution of different cues to visual attention. It reports the results of a quantitative comparison of human visual attention derived from fixation patterns with visual attention as modeled by different versions of the computer model. More specifically, a one-cue gray-level model is compared to a two-cues color model. The experiments conducted with over 40 images of different nature and involving 20 human subjects assess the quantitative contribution of chromatic features in visual attention.