Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation
We propose graph-driven approaches to image segmentation by developing diffusion processes defined on arbitrary graphs. We formulate a solution to the image segmentation problem modeled as the result of infectious wavefronts propagating on an image-driven graph, where pixels correspond to nodes of a...
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| Autor*in: |
Bampis, Christos G [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on image processing - New York, NY : Inst., 1992, 26(2017), 1, Seite 35-50 |
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| Übergeordnetes Werk: |
volume:26 ; year:2017 ; number:1 ; pages:35-50 |
| Links: |
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| DOI / URN: |
10.1109/TIP.2016.2621663 |
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OLC1989357725 |
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| 520 | |a We propose graph-driven approaches to image segmentation by developing diffusion processes defined on arbitrary graphs. We formulate a solution to the image segmentation problem modeled as the result of infectious wavefronts propagating on an image-driven graph, where pixels correspond to nodes of an arbitrary graph. By relating the popular susceptible-infected-recovered epidemic propagation model to the Random Walker algorithm, we develop the normalized random walker and a lazy random walker variant. The underlying iterative solutions of these methods are derived as the result of infections transmitted on this arbitrary graph. The main idea is to incorporate a degree-aware term into the original Random Walker algorithm in order to account for the node centrality of every neighboring node and to weigh the contribution of every neighbor to the underlying diffusion process. Our lazy random walk variant models the tendency of patients or nodes to resist changes in their infection status. We also show how previous work can be naturally extended to take advantage of this degree-aware term, which enables the design of other novel methods. Through an extensive experimental analysis, we demonstrate the reliability of our approach, its small computational burden and the dimensionality reduction capabilities of graph-driven approaches. Without applying any regular grid constraint, the proposed graph clustering scheme allows us to consider pixel-level, node-level approaches, and multidimensional input data by naturally integrating the importance of each node to the final clustering or segmentation solution. A software release containing implementations of this paper and supplementary material can be found at: <uri xlink:type="simple">http://cvsp.cs.ntua.gr/research/GraphClustering/ . | ||
| 650 | 4 | |a SIR epidemic propagation model | |
| 650 | 4 | |a diffusion modeling | |
| 650 | 4 | |a Graph clustering | |
| 650 | 4 | |a Diffusion processes | |
| 650 | 4 | |a Laplace equations | |
| 650 | 4 | |a random walker | |
| 650 | 4 | |a Algorithm design and analysis | |
| 650 | 4 | |a Steady-state | |
| 650 | 4 | |a Visualization | |
| 650 | 4 | |a Mathematical model | |
| 650 | 4 | |a Image segmentation | |
| 700 | 1 | |a Maragos, Petros |4 oth | |
| 700 | 1 | |a Bovik, Alan C |4 oth | |
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10.1109/TIP.2016.2621663 doi PQ20170501 (DE-627)OLC1989357725 (DE-599)GBVOLC1989357725 (PRQ)c708-23bb2ca76b481d7c2c78ad3dc8c9d4974caa435f7a1cd69e175dd64b5f02f2710 (KEY)0213811520170000026000100035graphdrivendiffusionandrandomwalkschemesforimagese DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Bampis, Christos G verfasserin aut Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose graph-driven approaches to image segmentation by developing diffusion processes defined on arbitrary graphs. We formulate a solution to the image segmentation problem modeled as the result of infectious wavefronts propagating on an image-driven graph, where pixels correspond to nodes of an arbitrary graph. By relating the popular susceptible-infected-recovered epidemic propagation model to the Random Walker algorithm, we develop the normalized random walker and a lazy random walker variant. The underlying iterative solutions of these methods are derived as the result of infections transmitted on this arbitrary graph. The main idea is to incorporate a degree-aware term into the original Random Walker algorithm in order to account for the node centrality of every neighboring node and to weigh the contribution of every neighbor to the underlying diffusion process. Our lazy random walk variant models the tendency of patients or nodes to resist changes in their infection status. We also show how previous work can be naturally extended to take advantage of this degree-aware term, which enables the design of other novel methods. Through an extensive experimental analysis, we demonstrate the reliability of our approach, its small computational burden and the dimensionality reduction capabilities of graph-driven approaches. Without applying any regular grid constraint, the proposed graph clustering scheme allows us to consider pixel-level, node-level approaches, and multidimensional input data by naturally integrating the importance of each node to the final clustering or segmentation solution. A software release containing implementations of this paper and supplementary material can be found at: <uri xlink:type="simple">http://cvsp.cs.ntua.gr/research/GraphClustering/ . SIR epidemic propagation model diffusion modeling Graph clustering Diffusion processes Laplace equations random walker Algorithm design and analysis Steady-state Visualization Mathematical model Image segmentation Maragos, Petros oth Bovik, Alan C oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 26(2017), 1, Seite 35-50 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:26 year:2017 number:1 pages:35-50 http://dx.doi.org/10.1109/TIP.2016.2621663 Volltext http://ieeexplore.ieee.org/document/7707309 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 54.00 AVZ AR 26 2017 1 35-50 |
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10.1109/TIP.2016.2621663 doi PQ20170501 (DE-627)OLC1989357725 (DE-599)GBVOLC1989357725 (PRQ)c708-23bb2ca76b481d7c2c78ad3dc8c9d4974caa435f7a1cd69e175dd64b5f02f2710 (KEY)0213811520170000026000100035graphdrivendiffusionandrandomwalkschemesforimagese DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Bampis, Christos G verfasserin aut Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose graph-driven approaches to image segmentation by developing diffusion processes defined on arbitrary graphs. We formulate a solution to the image segmentation problem modeled as the result of infectious wavefronts propagating on an image-driven graph, where pixels correspond to nodes of an arbitrary graph. By relating the popular susceptible-infected-recovered epidemic propagation model to the Random Walker algorithm, we develop the normalized random walker and a lazy random walker variant. The underlying iterative solutions of these methods are derived as the result of infections transmitted on this arbitrary graph. The main idea is to incorporate a degree-aware term into the original Random Walker algorithm in order to account for the node centrality of every neighboring node and to weigh the contribution of every neighbor to the underlying diffusion process. Our lazy random walk variant models the tendency of patients or nodes to resist changes in their infection status. We also show how previous work can be naturally extended to take advantage of this degree-aware term, which enables the design of other novel methods. Through an extensive experimental analysis, we demonstrate the reliability of our approach, its small computational burden and the dimensionality reduction capabilities of graph-driven approaches. Without applying any regular grid constraint, the proposed graph clustering scheme allows us to consider pixel-level, node-level approaches, and multidimensional input data by naturally integrating the importance of each node to the final clustering or segmentation solution. A software release containing implementations of this paper and supplementary material can be found at: <uri xlink:type="simple">http://cvsp.cs.ntua.gr/research/GraphClustering/ . SIR epidemic propagation model diffusion modeling Graph clustering Diffusion processes Laplace equations random walker Algorithm design and analysis Steady-state Visualization Mathematical model Image segmentation Maragos, Petros oth Bovik, Alan C oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 26(2017), 1, Seite 35-50 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:26 year:2017 number:1 pages:35-50 http://dx.doi.org/10.1109/TIP.2016.2621663 Volltext http://ieeexplore.ieee.org/document/7707309 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 54.00 AVZ AR 26 2017 1 35-50 |
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10.1109/TIP.2016.2621663 doi PQ20170501 (DE-627)OLC1989357725 (DE-599)GBVOLC1989357725 (PRQ)c708-23bb2ca76b481d7c2c78ad3dc8c9d4974caa435f7a1cd69e175dd64b5f02f2710 (KEY)0213811520170000026000100035graphdrivendiffusionandrandomwalkschemesforimagese DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Bampis, Christos G verfasserin aut Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose graph-driven approaches to image segmentation by developing diffusion processes defined on arbitrary graphs. We formulate a solution to the image segmentation problem modeled as the result of infectious wavefronts propagating on an image-driven graph, where pixels correspond to nodes of an arbitrary graph. By relating the popular susceptible-infected-recovered epidemic propagation model to the Random Walker algorithm, we develop the normalized random walker and a lazy random walker variant. The underlying iterative solutions of these methods are derived as the result of infections transmitted on this arbitrary graph. The main idea is to incorporate a degree-aware term into the original Random Walker algorithm in order to account for the node centrality of every neighboring node and to weigh the contribution of every neighbor to the underlying diffusion process. Our lazy random walk variant models the tendency of patients or nodes to resist changes in their infection status. We also show how previous work can be naturally extended to take advantage of this degree-aware term, which enables the design of other novel methods. Through an extensive experimental analysis, we demonstrate the reliability of our approach, its small computational burden and the dimensionality reduction capabilities of graph-driven approaches. Without applying any regular grid constraint, the proposed graph clustering scheme allows us to consider pixel-level, node-level approaches, and multidimensional input data by naturally integrating the importance of each node to the final clustering or segmentation solution. A software release containing implementations of this paper and supplementary material can be found at: <uri xlink:type="simple">http://cvsp.cs.ntua.gr/research/GraphClustering/ . SIR epidemic propagation model diffusion modeling Graph clustering Diffusion processes Laplace equations random walker Algorithm design and analysis Steady-state Visualization Mathematical model Image segmentation Maragos, Petros oth Bovik, Alan C oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 26(2017), 1, Seite 35-50 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:26 year:2017 number:1 pages:35-50 http://dx.doi.org/10.1109/TIP.2016.2621663 Volltext http://ieeexplore.ieee.org/document/7707309 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 54.00 AVZ AR 26 2017 1 35-50 |
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10.1109/TIP.2016.2621663 doi PQ20170501 (DE-627)OLC1989357725 (DE-599)GBVOLC1989357725 (PRQ)c708-23bb2ca76b481d7c2c78ad3dc8c9d4974caa435f7a1cd69e175dd64b5f02f2710 (KEY)0213811520170000026000100035graphdrivendiffusionandrandomwalkschemesforimagese DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Bampis, Christos G verfasserin aut Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose graph-driven approaches to image segmentation by developing diffusion processes defined on arbitrary graphs. We formulate a solution to the image segmentation problem modeled as the result of infectious wavefronts propagating on an image-driven graph, where pixels correspond to nodes of an arbitrary graph. By relating the popular susceptible-infected-recovered epidemic propagation model to the Random Walker algorithm, we develop the normalized random walker and a lazy random walker variant. The underlying iterative solutions of these methods are derived as the result of infections transmitted on this arbitrary graph. The main idea is to incorporate a degree-aware term into the original Random Walker algorithm in order to account for the node centrality of every neighboring node and to weigh the contribution of every neighbor to the underlying diffusion process. Our lazy random walk variant models the tendency of patients or nodes to resist changes in their infection status. We also show how previous work can be naturally extended to take advantage of this degree-aware term, which enables the design of other novel methods. Through an extensive experimental analysis, we demonstrate the reliability of our approach, its small computational burden and the dimensionality reduction capabilities of graph-driven approaches. Without applying any regular grid constraint, the proposed graph clustering scheme allows us to consider pixel-level, node-level approaches, and multidimensional input data by naturally integrating the importance of each node to the final clustering or segmentation solution. A software release containing implementations of this paper and supplementary material can be found at: <uri xlink:type="simple">http://cvsp.cs.ntua.gr/research/GraphClustering/ . SIR epidemic propagation model diffusion modeling Graph clustering Diffusion processes Laplace equations random walker Algorithm design and analysis Steady-state Visualization Mathematical model Image segmentation Maragos, Petros oth Bovik, Alan C oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 26(2017), 1, Seite 35-50 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:26 year:2017 number:1 pages:35-50 http://dx.doi.org/10.1109/TIP.2016.2621663 Volltext http://ieeexplore.ieee.org/document/7707309 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 54.00 AVZ AR 26 2017 1 35-50 |
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10.1109/TIP.2016.2621663 doi PQ20170501 (DE-627)OLC1989357725 (DE-599)GBVOLC1989357725 (PRQ)c708-23bb2ca76b481d7c2c78ad3dc8c9d4974caa435f7a1cd69e175dd64b5f02f2710 (KEY)0213811520170000026000100035graphdrivendiffusionandrandomwalkschemesforimagese DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Bampis, Christos G verfasserin aut Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose graph-driven approaches to image segmentation by developing diffusion processes defined on arbitrary graphs. We formulate a solution to the image segmentation problem modeled as the result of infectious wavefronts propagating on an image-driven graph, where pixels correspond to nodes of an arbitrary graph. By relating the popular susceptible-infected-recovered epidemic propagation model to the Random Walker algorithm, we develop the normalized random walker and a lazy random walker variant. The underlying iterative solutions of these methods are derived as the result of infections transmitted on this arbitrary graph. The main idea is to incorporate a degree-aware term into the original Random Walker algorithm in order to account for the node centrality of every neighboring node and to weigh the contribution of every neighbor to the underlying diffusion process. Our lazy random walk variant models the tendency of patients or nodes to resist changes in their infection status. We also show how previous work can be naturally extended to take advantage of this degree-aware term, which enables the design of other novel methods. Through an extensive experimental analysis, we demonstrate the reliability of our approach, its small computational burden and the dimensionality reduction capabilities of graph-driven approaches. Without applying any regular grid constraint, the proposed graph clustering scheme allows us to consider pixel-level, node-level approaches, and multidimensional input data by naturally integrating the importance of each node to the final clustering or segmentation solution. A software release containing implementations of this paper and supplementary material can be found at: <uri xlink:type="simple">http://cvsp.cs.ntua.gr/research/GraphClustering/ . SIR epidemic propagation model diffusion modeling Graph clustering Diffusion processes Laplace equations random walker Algorithm design and analysis Steady-state Visualization Mathematical model Image segmentation Maragos, Petros oth Bovik, Alan C oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 26(2017), 1, Seite 35-50 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:26 year:2017 number:1 pages:35-50 http://dx.doi.org/10.1109/TIP.2016.2621663 Volltext http://ieeexplore.ieee.org/document/7707309 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 54.00 AVZ AR 26 2017 1 35-50 |
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Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation |
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We propose graph-driven approaches to image segmentation by developing diffusion processes defined on arbitrary graphs. We formulate a solution to the image segmentation problem modeled as the result of infectious wavefronts propagating on an image-driven graph, where pixels correspond to nodes of an arbitrary graph. By relating the popular susceptible-infected-recovered epidemic propagation model to the Random Walker algorithm, we develop the normalized random walker and a lazy random walker variant. The underlying iterative solutions of these methods are derived as the result of infections transmitted on this arbitrary graph. The main idea is to incorporate a degree-aware term into the original Random Walker algorithm in order to account for the node centrality of every neighboring node and to weigh the contribution of every neighbor to the underlying diffusion process. Our lazy random walk variant models the tendency of patients or nodes to resist changes in their infection status. We also show how previous work can be naturally extended to take advantage of this degree-aware term, which enables the design of other novel methods. Through an extensive experimental analysis, we demonstrate the reliability of our approach, its small computational burden and the dimensionality reduction capabilities of graph-driven approaches. Without applying any regular grid constraint, the proposed graph clustering scheme allows us to consider pixel-level, node-level approaches, and multidimensional input data by naturally integrating the importance of each node to the final clustering or segmentation solution. A software release containing implementations of this paper and supplementary material can be found at: <uri xlink:type="simple">http://cvsp.cs.ntua.gr/research/GraphClustering/ . |
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We propose graph-driven approaches to image segmentation by developing diffusion processes defined on arbitrary graphs. We formulate a solution to the image segmentation problem modeled as the result of infectious wavefronts propagating on an image-driven graph, where pixels correspond to nodes of an arbitrary graph. By relating the popular susceptible-infected-recovered epidemic propagation model to the Random Walker algorithm, we develop the normalized random walker and a lazy random walker variant. The underlying iterative solutions of these methods are derived as the result of infections transmitted on this arbitrary graph. The main idea is to incorporate a degree-aware term into the original Random Walker algorithm in order to account for the node centrality of every neighboring node and to weigh the contribution of every neighbor to the underlying diffusion process. Our lazy random walk variant models the tendency of patients or nodes to resist changes in their infection status. We also show how previous work can be naturally extended to take advantage of this degree-aware term, which enables the design of other novel methods. Through an extensive experimental analysis, we demonstrate the reliability of our approach, its small computational burden and the dimensionality reduction capabilities of graph-driven approaches. Without applying any regular grid constraint, the proposed graph clustering scheme allows us to consider pixel-level, node-level approaches, and multidimensional input data by naturally integrating the importance of each node to the final clustering or segmentation solution. A software release containing implementations of this paper and supplementary material can be found at: <uri xlink:type="simple">http://cvsp.cs.ntua.gr/research/GraphClustering/ . |
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We propose graph-driven approaches to image segmentation by developing diffusion processes defined on arbitrary graphs. We formulate a solution to the image segmentation problem modeled as the result of infectious wavefronts propagating on an image-driven graph, where pixels correspond to nodes of an arbitrary graph. By relating the popular susceptible-infected-recovered epidemic propagation model to the Random Walker algorithm, we develop the normalized random walker and a lazy random walker variant. The underlying iterative solutions of these methods are derived as the result of infections transmitted on this arbitrary graph. The main idea is to incorporate a degree-aware term into the original Random Walker algorithm in order to account for the node centrality of every neighboring node and to weigh the contribution of every neighbor to the underlying diffusion process. Our lazy random walk variant models the tendency of patients or nodes to resist changes in their infection status. We also show how previous work can be naturally extended to take advantage of this degree-aware term, which enables the design of other novel methods. Through an extensive experimental analysis, we demonstrate the reliability of our approach, its small computational burden and the dimensionality reduction capabilities of graph-driven approaches. Without applying any regular grid constraint, the proposed graph clustering scheme allows us to consider pixel-level, node-level approaches, and multidimensional input data by naturally integrating the importance of each node to the final clustering or segmentation solution. A software release containing implementations of this paper and supplementary material can be found at: <uri xlink:type="simple">http://cvsp.cs.ntua.gr/research/GraphClustering/ . |
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