Saliency Detection using Geometric Context Contrast Inferred from Natural Images
Anurag Singh, Chee-Hung Henry Chu, Michael A. Pratt
2015
Abstract
Image saliency detection using region contrast is often based on the premise that salient region has a contrast with the background which becomes a limiting factor if the color of the salient object background is similar. To overcome this problem associated with single image analysis, we propose to collect background regions from a collection of images where generative property of, say, natural images ensures that all the images are spun out of it hence negating any bias. Background regions are differentiated based on their geometric context where we use the ground and sky context as background. Finally, the aggregated map is generated using color contrast between the superpixels segments of the image and collection of background superpixels.
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Paper Citation
in Harvard Style
Singh A., Henry Chu C. and A. Pratt M. (2015). Saliency Detection using Geometric Context Contrast Inferred from Natural Images . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 609-616. DOI: 10.5220/0005316906090616
in Bibtex Style
@conference{visapp15,
author={Anurag Singh and Chee-Hung Henry Chu and Michael A. Pratt},
title={Saliency Detection using Geometric Context Contrast Inferred from Natural Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={609-616},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005316906090616},
isbn={978-989-758-089-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Saliency Detection using Geometric Context Contrast Inferred from Natural Images
SN - 978-989-758-089-5
AU - Singh A.
AU - Henry Chu C.
AU - A. Pratt M.
PY - 2015
SP - 609
EP - 616
DO - 10.5220/0005316906090616