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Authors: Anurag Singh ; Chee-Hung Henry Chu and Michael A. Pratt

Affiliation: University of Louisiana at Lafayette, United States

Keyword(s): Visual Saliency, Geometric Context, Large Image Collections, Superpixels.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Segmentation and Grouping ; Visual Attention and Image Saliency

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 several formats:
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 (VISIGRAPP 2015) - Volume 2: VISAPP; ISBN 978-989-758-089-5; ISSN 2184-4321, SciTePress, pages 609-616. DOI: 10.5220/0005316906090616

@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 (VISIGRAPP 2015) - Volume 2: VISAPP},
year={2015},
pages={609-616},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005316906090616},
isbn={978-989-758-089-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 2: VISAPP
TI - Saliency Detection using Geometric Context Contrast Inferred from Natural Images
SN - 978-989-758-089-5
IS - 2184-4321
AU - Singh, A.
AU - Henry Chu, C.
AU - A. Pratt, M.
PY - 2015
SP - 609
EP - 616
DO - 10.5220/0005316906090616
PB - SciTePress

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