Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale...
Ausführliche Beschreibung
Autor*in: |
Scharfenberger, Christian [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2015 |
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Übergeordnetes Werk: |
Enthalten in: IEEE transactions on image processing - New York, NY : Inst., 1992, 24(2015), 1, Seite 457-470 |
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Übergeordnetes Werk: |
volume:24 ; year:2015 ; number:1 ; pages:457-470 |
Links: |
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DOI / URN: |
10.1109/TIP.2014.2380351 |
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Katalog-ID: |
OLC1959239325 |
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520 | |a We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. | ||
650 | 4 | |a multilayer approach | |
650 | 4 | |a Image resolution | |
650 | 4 | |a saliency maps | |
650 | 4 | |a Atomic layer deposition | |
650 | 4 | |a structured image elements | |
650 | 4 | |a natural images | |
650 | 4 | |a Computational modeling | |
650 | 4 | |a performance evaluation metrics | |
650 | 4 | |a image texture | |
650 | 4 | |a Statistical textural distinctiveness | |
650 | 4 | |a probability | |
650 | 4 | |a salient region detection approach | |
650 | 4 | |a statistical textural distinctiveness matrix | |
650 | 4 | |a structure | |
650 | 4 | |a public data sets | |
650 | 4 | |a feature extraction | |
650 | 4 | |a salient region detection | |
650 | 4 | |a edge detection | |
650 | 4 | |a sparse texture modeling | |
650 | 4 | |a statistical analysis | |
650 | 4 | |a Image color analysis | |
650 | 4 | |a Image segmentation | |
650 | 4 | |a rotational-invariant neighborhood-based textural representations | |
650 | 4 | |a structure-guided statistical textural distinctiveness approach | |
650 | 4 | |a representative texture atom pairs | |
650 | 4 | |a occurrence probability | |
650 | 4 | |a image representation | |
700 | 1 | |a Wong, Alexander |4 oth | |
700 | 1 | |a Clausi, David A |4 oth | |
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10.1109/TIP.2014.2380351 doi PQ20160617 (DE-627)OLC1959239325 (DE-599)GBVOLC1959239325 (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0 (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Scharfenberger, Christian verfasserin aut Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation Wong, Alexander oth Clausi, David A oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 457-470 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:457-470 http://dx.doi.org/10.1109/TIP.2014.2380351 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068 http://www.ncbi.nlm.nih.gov/pubmed/25695960 http://search.proquest.com/docview/1640798317 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 457-470 |
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10.1109/TIP.2014.2380351 doi PQ20160617 (DE-627)OLC1959239325 (DE-599)GBVOLC1959239325 (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0 (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Scharfenberger, Christian verfasserin aut Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation Wong, Alexander oth Clausi, David A oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 457-470 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:457-470 http://dx.doi.org/10.1109/TIP.2014.2380351 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068 http://www.ncbi.nlm.nih.gov/pubmed/25695960 http://search.proquest.com/docview/1640798317 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 457-470 |
allfields_unstemmed |
10.1109/TIP.2014.2380351 doi PQ20160617 (DE-627)OLC1959239325 (DE-599)GBVOLC1959239325 (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0 (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Scharfenberger, Christian verfasserin aut Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation Wong, Alexander oth Clausi, David A oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 457-470 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:457-470 http://dx.doi.org/10.1109/TIP.2014.2380351 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068 http://www.ncbi.nlm.nih.gov/pubmed/25695960 http://search.proquest.com/docview/1640798317 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 457-470 |
allfieldsGer |
10.1109/TIP.2014.2380351 doi PQ20160617 (DE-627)OLC1959239325 (DE-599)GBVOLC1959239325 (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0 (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Scharfenberger, Christian verfasserin aut Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation Wong, Alexander oth Clausi, David A oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 457-470 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:457-470 http://dx.doi.org/10.1109/TIP.2014.2380351 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068 http://www.ncbi.nlm.nih.gov/pubmed/25695960 http://search.proquest.com/docview/1640798317 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 457-470 |
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10.1109/TIP.2014.2380351 doi PQ20160617 (DE-627)OLC1959239325 (DE-599)GBVOLC1959239325 (PRQ)c2334-3969f4b56b2865aa9b7dac7c3de8c04abd5a77a2f2b536216b4ab398ec3ac13c0 (KEY)0213811520150000024000100457structureguidedstatisticaltexturaldistinctivenessf DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Scharfenberger, Christian verfasserin aut Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation Wong, Alexander oth Clausi, David A oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 457-470 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:457-470 http://dx.doi.org/10.1109/TIP.2014.2380351 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980068 http://www.ncbi.nlm.nih.gov/pubmed/25695960 http://search.proquest.com/docview/1640798317 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 457-470 |
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multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation |
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Scharfenberger, Christian @@aut@@ Wong, Alexander @@oth@@ Clausi, David A @@oth@@ |
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Scharfenberger, Christian ddc 004 bkl 54.00 misc multilayer approach misc Image resolution misc saliency maps misc Atomic layer deposition misc structured image elements misc natural images misc Computational modeling misc performance evaluation metrics misc image texture misc Statistical textural distinctiveness misc probability misc salient region detection approach misc statistical textural distinctiveness matrix misc structure misc public data sets misc feature extraction misc salient region detection misc edge detection misc sparse texture modeling misc statistical analysis misc Image color analysis misc Image segmentation misc rotational-invariant neighborhood-based textural representations misc structure-guided statistical textural distinctiveness approach misc representative texture atom pairs misc occurrence probability misc image representation Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images |
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004 620 DNB 54.00 bkl Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images multilayer approach Image resolution saliency maps Atomic layer deposition structured image elements natural images Computational modeling performance evaluation metrics image texture Statistical textural distinctiveness probability salient region detection approach statistical textural distinctiveness matrix structure public data sets feature extraction salient region detection edge detection sparse texture modeling statistical analysis Image color analysis Image segmentation rotational-invariant neighborhood-based textural representations structure-guided statistical textural distinctiveness approach representative texture atom pairs occurrence probability image representation |
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ddc 004 bkl 54.00 misc multilayer approach misc Image resolution misc saliency maps misc Atomic layer deposition misc structured image elements misc natural images misc Computational modeling misc performance evaluation metrics misc image texture misc Statistical textural distinctiveness misc probability misc salient region detection approach misc statistical textural distinctiveness matrix misc structure misc public data sets misc feature extraction misc salient region detection misc edge detection misc sparse texture modeling misc statistical analysis misc Image color analysis misc Image segmentation misc rotational-invariant neighborhood-based textural representations misc structure-guided statistical textural distinctiveness approach misc representative texture atom pairs misc occurrence probability misc image representation |
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structure-guided statistical textural distinctiveness for salient region detection in natural images |
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Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images |
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We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. |
abstractGer |
We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. |
abstract_unstemmed |
We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches. |
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Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images |
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