Abstract
Visual attention is the ability of a vision system, be it biological or artificial, to rapidly detect potentially relevant parts of a visual scene. The saliency-based model of visual attention is widely used to simulate this visual mechanism on computers. Though biologically inspired, this model has been only partially assessed in comparison with human behavior. The research described in this paper aims at assessing its performance in the case of natural scenes, i.e. real 3D color scenes. The evaluation is based on the comparison of computer saliency maps with human visual attention derived from fixation patterns while subjects are looking at the scenes. The paper presents a number of experiments involving natural scenes and computer models differing by their capacity to deal with color and depth. The results point on the large range of scene specific performance variations and provide typical quantitative performance values for models of different complexity.
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Hügli, H., Jost, T., Ouerhani, N. (2005). Model Performance for Visual Attention in Real 3D Color Scenes. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_48
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DOI: https://doi.org/10.1007/11499305_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26319-7
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