Saliency Detection Using Min-cut Proposals Framework

DOI

10.2991/icmmita-16.2016.302How to use a DOI?

Keywords

Saliency Detection; Objectness Proposals; Min-cut; SVM.

Abstract

In saliency detection, almost all the approaches map an image into a graph and assign the saliency value to each element, e.g. pixel, region or superpixel. In this paper, we first utilize a series of image features among superpixels in the support vector machine (SVM) to train linear predicted models. For a well-performance model we take cross validation in the supervised learning. Then, we take the SVM regression models to predict initial saliency maps, while using SVM classifier to get the foreground and background seeds. Besides, we employ an objectness min-cut algorithm to obtain the segments of different proposals. Finally, after ranking these proposals, we select the top one integrating with the initial maps to achieve the final saliency maps. The proposed approach is tested extensively on four different databases and then compared with existing algorithms.

Copyright

© 2017, 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  - Meiling Sun
AU  - Fengxia Li
AU  - Sanyuan Zhao
AU  - Da Huo
AU  - Chenguang Yang
PY  - 2017/01
DA  - 2017/01
TI  - Saliency Detection Using Min-cut Proposals Framework
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 1339
EP  - 1344
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.302
DO  - 10.2991/icmmita-16.2016.302
ID  - Sun2017/01
ER  -