基于鲁棒前景选择的显著性检测

王晨; 樊养余; 李波

doi:10.11999/JEIT170390

基于鲁棒前景选择的显著性检测

doi: 10.11999/JEIT170390

2.
(西北工业大学电子信息学院西安 710072) ②(空军工程大学航空航天工程学院西安 710038)

基金项目:

国家自然科学基金(61379104)

计量
- 文章访问数: 1120
- HTML全文浏览量: 131
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- 被引次数: 0
出版历程
- 收稿日期: 2017-04-26
- 修回日期: 2017-07-17
- 刊出日期: 2017-11-19

Saliency Detection Based on Robust Foreground Selection

2.
(School of Electronics &

Funds:

The National Natural Science Foundation of China (61379104)

摘要

摘要: 显著性检测是指自动提取未知场景中符合人类视觉习惯的兴趣目标的方法。为了进一步提高检测的准确性，该文提出了利用鲁棒前景种子的流形排序进行显著性检测的算法。首先利用角点检测和边缘连接算法得到两个不同的凸包，用它们的交集初步确立目标区域的大致位置；然后利用凸包外边缘作为标准对凸包内的超像素进行相似度检测，将与大部分外边缘相似的超像素去除，得到更准确的目标样本作为前景种子；利用锚点图构建新的图结构表示数据节点之间的关系；接着通过基于前景和背景种子的流形排序算法对图像所有区域进行排序，并得到两种不同的显著性检测图；最后借助代价函数对显著性图进行优化，得到最终的显著性检测结果。经实验表明，与几种经典算法对比，该文方法可以进一步提高显著性算法的精确度和召回率。
- 显著性检测 /
- 凸包 /
- 锚点图 /
- 流形排序
Abstract: Saliency detection is to find the most important object automatically according to the human visual in the unknown scene. For improving the precision of saliency detection, the saliency detection based on robust foreground seeds via manifold ranking is proposed in this paper. Firstly, the two different convex hulls are got by the Harris corner and boundary connectivity algorithm. And the original object region is defined by the intersection about the above convex hulls. Secondly, the superpixels in convex hull are done the similarity detection with the outer edge of the convex hull. The superpixels are removed when they are similar to most of the outer edge, and the more precision foreground seeds are got. Using the anchor graph, a novel graph construction is built to express the relationship between data nodes. And then, two different kinds of salient results will be got based on ranking on manifolds using foreground and background seeds respectively. Finally, the saliency map is got through optimizing a novel cost function. Experimental results prove that the proposed algorithm improves the performance evaluation of precision and recall rate further.
- Saliency detection /
- Convex hull /
- Anchor graph /
- Manifold ranking

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参考文献(31)

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