.png){kind=icon}
返回
Salient object detection via cross diffusion-based compactness on multiple graphs
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-021-10568-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The diffusion-based graph has been widely used in saliency detection. Most of the existing methods treat the image boundary patches as background seeds, which may result in the imprecise saliency map if the salient region touches the image boundaries. In this paper, we propose a salient object detection approach via cross diffusion-based compactness on multiple graphs. Firstly, we extract multi-view image features including low-level image features, mid-level image cues (low-level saliency priors), and background-based saliency map. Then, we compute the respective similarity matrix to construct the corresponding graph, and a cross-diffusion algorithm is presented that diffuses each similarity matrix on other's graphs rather than at itself graph, helping to the compactness-based saliency maps. Additionally, for well propagating the saliency values, we model a propagation mechanism based on cellular automata, by linearly incorporating low-level image features and mid-level image cues together to generate a superior impact factor matrix. Extensive experiment results demonstrate that the proposed method achieves better saliency detection performance against the unsupervised state-of-the-art methods on three public datasets.
Keyword:
Salient object detection
Cross diffusion-based compactness
Multi-view image informations
Multiple k-regular graphs
Multi-view SCA model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
暂无机构信息
引用论文
A simple saliency detection approach via automatic top-down feature fusion一种简单的自顶向下特征融合显著性检测方法.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
分享收藏Exploiting background divergence and foreground compactness for salient object detectionNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容