2006
NIPS
NeurIPS 2006
Graph-Based Visual Saliency
Abstract
A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two steps: rst forming activation maps on certain feature channels, and then normalizing them in a way which highlights conspicuity and admits combination with other maps. The model is simple, and biologically plausible insofar as it is naturally parallelized. This model powerfully predicts human xations on 749 variations of 108 natural images, achieving 98% of the ROC area of a human-based control, whereas the classical algorithms of Itti & Koch ([2], [3], [4]) achieve only 84%.
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Conference Pioneer
— NIPS 2006
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Trend Setter
— Image Segmentation
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Keyword Pioneer
— visual saliency
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Robotics
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Topic Pioneer
— Computer Vision
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Interdisciplinary Bridge
— Artificial Intelligence and Computer Vision
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Hot Topic Early Bird
— scene understanding