Salient Region Detection Using Diffusion Process with Nonlocal Connections Article Swipe
Huiyuan Luo
,
Guangliang Han
,
Peixun Liu
,
Yanfeng Wu
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/app8122526
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/app8122526
Diffusion-based salient region detection methods have gained great popularity. In most diffusion-based methods, the saliency values are ranked on 2-layer neighborhood graph by connecting each node to its neighboring nodes and the nodes sharing common boundaries with its neighboring nodes. However, only considering the local relevance between neighbors, the salient region may be heterogeneous and even wrongly suppressed, especially when the features of salient object are diverse. In order to address the issue, we present an effective saliency detection method using diffusing process on the graph with nonlocal connections. First, a saliency-biased Gaussian model is used to refine the saliency map based on the compactness cue, and then, the saliency information of compactness is diffused on 2-layer sparse graph with nonlocal connections. Second, we obtain the contrast of each superpixel by restricting the reference region to the background. Similarly, a saliency-biased Gaussian refinement model is generated and the saliency information based on the uniqueness cue is propagated on the 2-layer sparse graph. We linearly integrate the initial saliency maps based on the compactness and uniqueness cues due to the complementarity to each other. Finally, to obtain a highlighted and homogeneous saliency map, a single-layer updating and multi-layer integrating scheme is presented. Comprehensive experiments on four benchmark datasets demonstrate that the proposed method performs better in terms of various evaluation metrics.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app8122526
- https://www.mdpi.com/2076-3417/8/12/2526/pdf?version=1544179849
- OA Status
- gold
- Cited By
- 6
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2905017881
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2905017881Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app8122526Digital Object Identifier
- Title
-
Salient Region Detection Using Diffusion Process with Nonlocal ConnectionsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-12-06Full publication date if available
- Authors
-
Huiyuan Luo, Guangliang Han, Peixun Liu, Yanfeng WuList of authors in order
- Landing page
-
https://doi.org/10.3390/app8122526Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/8/12/2526/pdf?version=1544179849Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/8/12/2526/pdf?version=1544179849Direct OA link when available
- Concepts
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Salient, Computer science, Compact space, Graph, Uniqueness, Artificial intelligence, Pattern recognition (psychology), Algorithm, Mathematics, Theoretical computer science, Pure mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 1, 2021: 2, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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