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基于带汇点Laplace扩散模型的显著目标检测

王宝艳 张铁 王新刚

王宝艳, 张铁, 王新刚. 基于带汇点Laplace扩散模型的显著目标检测[J]. 电子与信息学报, 2017, 39(8): 1934-1941. doi: 10.11999/JEIT161296
引用本文: 王宝艳, 张铁, 王新刚. 基于带汇点Laplace扩散模型的显著目标检测[J]. 电子与信息学报, 2017, 39(8): 1934-1941. doi: 10.11999/JEIT161296
WANG Baoyan, ZHANG Tie, WANG Xingang. Salient Object Detection Based on Laplace Diffusion Models with Sink Points[J]. Journal of Electronics and Information Technology, 2017, 39(8): 1934-1941. doi: 10.11999/JEIT161296
Citation: WANG Baoyan, ZHANG Tie, WANG Xingang. Salient Object Detection Based on Laplace Diffusion Models with Sink Points[J]. Journal of Electronics and Information Technology, 2017, 39(8): 1934-1941. doi: 10.11999/JEIT161296

基于带汇点Laplace扩散模型的显著目标检测

doi: 10.11999/JEIT161296
基金项目: 

国家自然科学基金(51475086),辽宁省自然科学基金(2014020026)

Salient Object Detection Based on Laplace Diffusion Models with Sink Points

Funds: 

The National Natural Science Foundation of China (51475086), The Natural Science Foundation of Liaoning Province (2014020026)

  • 摘要: 该文基于Laplace相似度量的构造方法,针对两阶段显著目标检测中显著种子的不同类型(稀疏或稠密),提出了相应的显著性扩散模型,从而实现了基于扩散的两阶段互补的显著目标检测。尤其是第2阶段扩散模型中汇点的融入,一方面更好地抑制了显著性图中的背景,同时对于控制因子的取值更加稳健。实验结果表明,当显著种子确定时,不同的扩散模型会导致显著性扩散程度的差异。基于带汇点Laplace的两阶段互补的扩散模型较其他扩散模型更有效、更稳健。同时,从多项评价指标分析,该算法与目前流行的5种显著目标检测算法相比,具有较大优势。这表明此种用于图像检索或分类的Laplace相似度量的构造方法在显著目标检测中也是适用的。
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    出版历程
    • 收稿日期:  2016-11-28
    • 修回日期:  2017-04-25
    • 刊出日期:  2017-08-19

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