A multiscale superpixel-level salient object detection model using local-global contrast cue
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- 作者：Nan Mu 穆 楠 ; Xin Xu 徐 新 ; Yinglin Wang 王英林…
- 关键词：Key wordssalient object detection ; superpixel ; multiple scales ; local contrast ; global contrast
- 刊名：Journal of Shanghai Jiaotong University (Science)
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：22
- 期：1
- 页码：121-128
- 全文大小：
- 刊物类别：Engineering
- 刊物主题：Electrical Engineering; Materials Science, general; Computer Science, general; Architecture, general; Life Sciences, general;
- 出版者：Shanghai Jiaotong University Press
- ISSN：1995-8188
- 卷排序：22
文摘
The goal of salient object detection is to estimate the regions which are most likely to attract human’s visual attention. As an important image preprocessing procedure to reduce the computational complexity, salient object detection is still a challenging problem in computer vision. In this paper, we proposed a salient object detection model by integrating local and global superpixel contrast at multiple scales. Three features are computed to estimate the saliency of superpixel. Two optimization measures are utilized to refine the resulting saliency map. Extensive experiments with the state-of-the-art saliency models on four public datasets demonstrate the effectiveness of the proposed model.