Contrast based Saliency Detection via Manifold Ranking

DOI

10.2991/isaeece-16.2016.61How to use a DOI?

Keywords

saliency detection, manifold ranking, superpixel segmentation, foreground region

Abstract

Saliency detectionhas been widely studied in computer vision. In this paper we propose a two-steps method combining contrast assumption and ranking technology to detect saliency region. Firstly, We use the traditional contrast assumption to find foregroundcues. Then we rank the nodes with labeled contrast cues bygraph-based manifold ranking. We experiment with our method on a large public data set. Our results show the effectiveness of our method, and perform better compared to recent state-of-the-art methods.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shunyao Jin
AU  - Xueqing Li
PY  - 2016/04
DA  - 2016/04
TI  - Contrast based Saliency Detection via Manifold Ranking
BT  - Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 317
EP  - 321
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-16.2016.61
DO  - 10.2991/isaeece-16.2016.61
ID  - Jin2016/04
ER  -