Saliency Detection Using Min-cut Proposals Framework
- 10.2991/icmmita-16.2016.302How to use a DOI?
- Saliency Detection; Objectness Proposals; Min-cut; SVM.
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.
- © 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/).
Cite this article
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 -