A graph based superpixel generation algorithm
Abstract In recent years, superpixels have become a prevailing tool in computer vision and many methods have been proposed. However, due to the problems such as high time complexity, low object boundary adherence and irregular shape, only a few methods are widely used. To improve these issues, we pr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Xiang [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 48(2018), 11 vom: 29. Juni, Seite 4485-4496 |
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| Übergeordnetes Werk: |
volume:48 ; year:2018 ; number:11 ; day:29 ; month:06 ; pages:4485-4496 |
| Links: |
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| DOI / URN: |
10.1007/s10489-018-1223-1 |
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OLC206610583X |
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| 520 | |a Abstract In recent years, superpixels have become a prevailing tool in computer vision and many methods have been proposed. However, due to the problems such as high time complexity, low object boundary adherence and irregular shape, only a few methods are widely used. To improve these issues, we propose a novel general superpixel segmentation method called minstpixel, which relies on energy functional minimization. Minstpixel introduces an energy functional based on minimal spanning tree and designs a strategy to gain the global optimum. It never needs sophisticated optimization scheme, complicated mathematical deduction or fussy iteration process. At the same time, the time complexity of minstpixel is approximately linear with respect to the number of image pixels. The benchmark on Berkeley segmentation database shows that minstpixel could rival state-of-the-art in every aspect. | ||
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| 700 | 1 | |a Zhao, Weidong |4 aut | |
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10.1007/s10489-018-1223-1 doi (DE-627)OLC206610583X (DE-He213)s10489-018-1223-1-p DE-627 ger DE-627 rakwb eng 004 VZ Wu, Xiang verfasserin aut A graph based superpixel generation algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In recent years, superpixels have become a prevailing tool in computer vision and many methods have been proposed. However, due to the problems such as high time complexity, low object boundary adherence and irregular shape, only a few methods are widely used. To improve these issues, we propose a novel general superpixel segmentation method called minstpixel, which relies on energy functional minimization. Minstpixel introduces an energy functional based on minimal spanning tree and designs a strategy to gain the global optimum. It never needs sophisticated optimization scheme, complicated mathematical deduction or fussy iteration process. At the same time, the time complexity of minstpixel is approximately linear with respect to the number of image pixels. The benchmark on Berkeley segmentation database shows that minstpixel could rival state-of-the-art in every aspect. Superpixels Energy minimization Minimization spanning tree Liu, Xianhui aut Chen, Yufei aut Shen, Jianan aut Zhao, Weidong aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 11 vom: 29. Juni, Seite 4485-4496 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:11 day:29 month:06 pages:4485-4496 https://doi.org/10.1007/s10489-018-1223-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 11 29 06 4485-4496 |
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10.1007/s10489-018-1223-1 doi (DE-627)OLC206610583X (DE-He213)s10489-018-1223-1-p DE-627 ger DE-627 rakwb eng 004 VZ Wu, Xiang verfasserin aut A graph based superpixel generation algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In recent years, superpixels have become a prevailing tool in computer vision and many methods have been proposed. However, due to the problems such as high time complexity, low object boundary adherence and irregular shape, only a few methods are widely used. To improve these issues, we propose a novel general superpixel segmentation method called minstpixel, which relies on energy functional minimization. Minstpixel introduces an energy functional based on minimal spanning tree and designs a strategy to gain the global optimum. It never needs sophisticated optimization scheme, complicated mathematical deduction or fussy iteration process. At the same time, the time complexity of minstpixel is approximately linear with respect to the number of image pixels. The benchmark on Berkeley segmentation database shows that minstpixel could rival state-of-the-art in every aspect. Superpixels Energy minimization Minimization spanning tree Liu, Xianhui aut Chen, Yufei aut Shen, Jianan aut Zhao, Weidong aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 11 vom: 29. Juni, Seite 4485-4496 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:11 day:29 month:06 pages:4485-4496 https://doi.org/10.1007/s10489-018-1223-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 11 29 06 4485-4496 |
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10.1007/s10489-018-1223-1 doi (DE-627)OLC206610583X (DE-He213)s10489-018-1223-1-p DE-627 ger DE-627 rakwb eng 004 VZ Wu, Xiang verfasserin aut A graph based superpixel generation algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In recent years, superpixels have become a prevailing tool in computer vision and many methods have been proposed. However, due to the problems such as high time complexity, low object boundary adherence and irregular shape, only a few methods are widely used. To improve these issues, we propose a novel general superpixel segmentation method called minstpixel, which relies on energy functional minimization. Minstpixel introduces an energy functional based on minimal spanning tree and designs a strategy to gain the global optimum. It never needs sophisticated optimization scheme, complicated mathematical deduction or fussy iteration process. At the same time, the time complexity of minstpixel is approximately linear with respect to the number of image pixels. The benchmark on Berkeley segmentation database shows that minstpixel could rival state-of-the-art in every aspect. Superpixels Energy minimization Minimization spanning tree Liu, Xianhui aut Chen, Yufei aut Shen, Jianan aut Zhao, Weidong aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 11 vom: 29. Juni, Seite 4485-4496 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:11 day:29 month:06 pages:4485-4496 https://doi.org/10.1007/s10489-018-1223-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 11 29 06 4485-4496 |
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10.1007/s10489-018-1223-1 doi (DE-627)OLC206610583X (DE-He213)s10489-018-1223-1-p DE-627 ger DE-627 rakwb eng 004 VZ Wu, Xiang verfasserin aut A graph based superpixel generation algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In recent years, superpixels have become a prevailing tool in computer vision and many methods have been proposed. However, due to the problems such as high time complexity, low object boundary adherence and irregular shape, only a few methods are widely used. To improve these issues, we propose a novel general superpixel segmentation method called minstpixel, which relies on energy functional minimization. Minstpixel introduces an energy functional based on minimal spanning tree and designs a strategy to gain the global optimum. It never needs sophisticated optimization scheme, complicated mathematical deduction or fussy iteration process. At the same time, the time complexity of minstpixel is approximately linear with respect to the number of image pixels. The benchmark on Berkeley segmentation database shows that minstpixel could rival state-of-the-art in every aspect. Superpixels Energy minimization Minimization spanning tree Liu, Xianhui aut Chen, Yufei aut Shen, Jianan aut Zhao, Weidong aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 11 vom: 29. Juni, Seite 4485-4496 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:11 day:29 month:06 pages:4485-4496 https://doi.org/10.1007/s10489-018-1223-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 11 29 06 4485-4496 |
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10.1007/s10489-018-1223-1 doi (DE-627)OLC206610583X (DE-He213)s10489-018-1223-1-p DE-627 ger DE-627 rakwb eng 004 VZ Wu, Xiang verfasserin aut A graph based superpixel generation algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In recent years, superpixels have become a prevailing tool in computer vision and many methods have been proposed. However, due to the problems such as high time complexity, low object boundary adherence and irregular shape, only a few methods are widely used. To improve these issues, we propose a novel general superpixel segmentation method called minstpixel, which relies on energy functional minimization. Minstpixel introduces an energy functional based on minimal spanning tree and designs a strategy to gain the global optimum. It never needs sophisticated optimization scheme, complicated mathematical deduction or fussy iteration process. At the same time, the time complexity of minstpixel is approximately linear with respect to the number of image pixels. The benchmark on Berkeley segmentation database shows that minstpixel could rival state-of-the-art in every aspect. Superpixels Energy minimization Minimization spanning tree Liu, Xianhui aut Chen, Yufei aut Shen, Jianan aut Zhao, Weidong aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 11 vom: 29. Juni, Seite 4485-4496 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:11 day:29 month:06 pages:4485-4496 https://doi.org/10.1007/s10489-018-1223-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 11 29 06 4485-4496 |
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Abstract In recent years, superpixels have become a prevailing tool in computer vision and many methods have been proposed. However, due to the problems such as high time complexity, low object boundary adherence and irregular shape, only a few methods are widely used. To improve these issues, we propose a novel general superpixel segmentation method called minstpixel, which relies on energy functional minimization. Minstpixel introduces an energy functional based on minimal spanning tree and designs a strategy to gain the global optimum. It never needs sophisticated optimization scheme, complicated mathematical deduction or fussy iteration process. At the same time, the time complexity of minstpixel is approximately linear with respect to the number of image pixels. The benchmark on Berkeley segmentation database shows that minstpixel could rival state-of-the-art in every aspect. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract In recent years, superpixels have become a prevailing tool in computer vision and many methods have been proposed. However, due to the problems such as high time complexity, low object boundary adherence and irregular shape, only a few methods are widely used. To improve these issues, we propose a novel general superpixel segmentation method called minstpixel, which relies on energy functional minimization. Minstpixel introduces an energy functional based on minimal spanning tree and designs a strategy to gain the global optimum. It never needs sophisticated optimization scheme, complicated mathematical deduction or fussy iteration process. At the same time, the time complexity of minstpixel is approximately linear with respect to the number of image pixels. The benchmark on Berkeley segmentation database shows that minstpixel could rival state-of-the-art in every aspect. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract In recent years, superpixels have become a prevailing tool in computer vision and many methods have been proposed. However, due to the problems such as high time complexity, low object boundary adherence and irregular shape, only a few methods are widely used. To improve these issues, we propose a novel general superpixel segmentation method called minstpixel, which relies on energy functional minimization. Minstpixel introduces an energy functional based on minimal spanning tree and designs a strategy to gain the global optimum. It never needs sophisticated optimization scheme, complicated mathematical deduction or fussy iteration process. At the same time, the time complexity of minstpixel is approximately linear with respect to the number of image pixels. The benchmark on Berkeley segmentation database shows that minstpixel could rival state-of-the-art in every aspect. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC206610583X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502205009.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-018-1223-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC206610583X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-018-1223-1-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wu, Xiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A graph based superpixel generation algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In recent years, superpixels have become a prevailing tool in computer vision and many methods have been proposed. However, due to the problems such as high time complexity, low object boundary adherence and irregular shape, only a few methods are widely used. To improve these issues, we propose a novel general superpixel segmentation method called minstpixel, which relies on energy functional minimization. Minstpixel introduces an energy functional based on minimal spanning tree and designs a strategy to gain the global optimum. It never needs sophisticated optimization scheme, complicated mathematical deduction or fussy iteration process. At the same time, the time complexity of minstpixel is approximately linear with respect to the number of image pixels. 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