基于低秩矩阵恢复的视觉显著性目标检测与细化
作者:
作者单位:

1.南京工程学院;2.韩山师范学院

作者简介:

通讯作者:

中图分类号:

TP391.4

基金项目:

南京工程学院科研基金项目(YKJ201722、JCYJ201825),广东省自然科学基金项目(No. 2016A030307050),广东省公益能力研究项目(No. 2016A020225008, No. 2017A040405062)


Salient Object Detection and Refinement based on Low Rank Matrix Recovery
Author:
Affiliation:

1.Nanjing Institute of Technology;2.Hanshan Normal University

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    基于低秩矩阵恢复(Low-Rank Matrix Recovery, LRMR)的显著性目标检测模型通过将图像特征分解为与背景关联的低秩分量和与显著性目标相关联的稀疏分量,并从稀疏分量中获得显著性目标.现有的显著性检测方法很少考虑低秩分量与稀疏分量之间的相互关系,导致检测的显著性目标零散或不完整.本文提出基于低秩矩阵恢复的显著性目标检测与细化方法来规避该限制.首先,本文所提方法采用 l1范数稀疏约束和拉普拉斯正则项对初始显著图进行计算;在显著性细化阶段,由于非局部的l0优化可以有效地对显著性区域及其邻接区域之间的相互关系进行建模,结合初始显著图,本文采用非局部l0梯度优化,最小化显著性区域中显著值的变化,从而保证显著性目标的完整性.本文在四个显著性目标检测数据集上进行实验,实验结果验证所提算法的优越性.

    Abstract:

    Low-Rank Matrix Recovery (LRMR) based salient object detection models decompose image features into a low-rank component associated with background and a sparse component associated with visual salient regions. Then, a saliency map can be generated from the saparse matrix. Existing LRMR-based saliency detection methods are prone to generating scattered or incomplete saliency maps which seldom considers inter-relationship among elements within these two components. A novel LRMR-based saliency detection algorithm is proposed to circumvent this limitation. Firstly, the proposed algorithm generates coarse saliency map by exploiting LRMR-based model that integrates a l1 norm sparsity constraint and a Laplacian regularization term. On the satge of saliency refinement, nonlocal l0 optimization is adopted to improve saliency map quality since it models not only the characteristics of an individual saliency but the interaction between neighbors. Based on the initial saliency map, the nonlocal l0 gradient is utilized to minimize the saliency variation in the salient object efficiently, thereby ensuring the integrity of the salient object. Experimental results on four public datasets of salient object detection validate the proposed algorithm superiority.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2019-12-23
  • 最后修改日期:2021-03-01
  • 录用日期:2020-03-18
  • 在线发布日期:
  • 出版日期: