Visual attention prediction for images with leading line structure

Visual attention prediction for images with leading line structure Researchers have proposed a wide variety of visual attention models, ranging from models that use local, low-level image features to recent approaches that incorporate semantic information. However, most models do not account for the visual attention evident in images with certain global structures. We focus specifically on “leading line” structures, in which explicit or implicit lines converge at a point. Through this study, we have conducted the experiments to investigate the visual attentions in images with leading line structure and propose new models that combine the low-level feature of center-surround differences of visual stimuli, the semantic feature of center bias and the structure feature of leading lines. We also create a new data set from 110 natural images containing leading lines and the eye-tracking data for 16 subjects. Our evaluation experiment showed that our models outperform the existing models against common indicators of saliency-map evaluation, underscoring the importance of leading lines in the modeling of visual attention. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Visual Computer Springer Journals

Visual attention prediction for images with leading line structure

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision
ISSN
0178-2789
eISSN
1432-2315
D.O.I.
10.1007/s00371-018-1518-6
Publisher site
See Article on Publisher Site

Abstract

Researchers have proposed a wide variety of visual attention models, ranging from models that use local, low-level image features to recent approaches that incorporate semantic information. However, most models do not account for the visual attention evident in images with certain global structures. We focus specifically on “leading line” structures, in which explicit or implicit lines converge at a point. Through this study, we have conducted the experiments to investigate the visual attentions in images with leading line structure and propose new models that combine the low-level feature of center-surround differences of visual stimuli, the semantic feature of center bias and the structure feature of leading lines. We also create a new data set from 110 natural images containing leading lines and the eye-tracking data for 16 subjects. Our evaluation experiment showed that our models outperform the existing models against common indicators of saliency-map evaluation, underscoring the importance of leading lines in the modeling of visual attention.

Journal

The Visual ComputerSpringer Journals

Published: May 5, 2018

References

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