Classification with NormalBoost: Case Study Traffic Sign Classification
NormalBoost is a new boosting algorithm which is capable of classifying amulti-dimensional binary class dataset. It adaptively combines several weakclassifiers to form a strong classifier. Unlike many boosting algorithmswhich have high computation and memory complexities, NormalBoost is capableof cl...
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| Autor*in: |
Fleyeh, Hasan [verfasserIn] Davami, Erfan [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Erschienen: |
Walter de Gruyter GmbH & Co. KG ; 2012 |
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| Schlagwörter: |
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| Umfang: |
19 |
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| Reproduktion: |
Walter de Gruyter Online Zeitschriften |
|---|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent systems - Berlin : de Gruyter, 1992, 21(2012), 1 vom: 29. Feb., Seite 25-43 |
| Übergeordnetes Werk: |
volume:21 ; year:2012 ; number:1 ; day:29 ; month:02 ; pages:25-43 ; extent:19 |
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| DOI / URN: |
10.1515/jisys-2012-0001 |
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NLEJ247092363 |
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| 520 | |a NormalBoost is a new boosting algorithm which is capable of classifying amulti-dimensional binary class dataset. It adaptively combines several weakclassifiers to form a strong classifier. Unlike many boosting algorithmswhich have high computation and memory complexities, NormalBoost is capableof classification with low complexity.The purpose of this paper is to present NormalBoost as a framework whichestablishes a platform to solve classification problems. The approach wastested with a dataset which was extracted automatically from real-worldtraffic sign images. The dataset contains both images of traffic signborders and speed limit pictograms. This framework involves the computationof Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images whichwere classified was 6500 binary images. A -fold validation was invoked tocheck the validity of the classification which resulted in a classificationrate of 98.4% and 98.9% being achieved for these two databases. Thisframework is distinguished by its invariance to in-plane rotation of theimages under consideration. Experiments show that the classification rateremains almost constant when traffic sign images with different angles ofrotations were tested. | ||
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10.1515/jisys-2012-0001 doi artikel_Grundlieferung.pp (DE-627)NLEJ247092363 DE-627 ger DE-627 rakwb Fleyeh, Hasan verfasserin aut Classification with NormalBoost: Case Study Traffic Sign Classification Walter de Gruyter GmbH & Co. KG 2012 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier NormalBoost is a new boosting algorithm which is capable of classifying amulti-dimensional binary class dataset. It adaptively combines several weakclassifiers to form a strong classifier. Unlike many boosting algorithmswhich have high computation and memory complexities, NormalBoost is capableof classification with low complexity.The purpose of this paper is to present NormalBoost as a framework whichestablishes a platform to solve classification problems. The approach wastested with a dataset which was extracted automatically from real-worldtraffic sign images. The dataset contains both images of traffic signborders and speed limit pictograms. This framework involves the computationof Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images whichwere classified was 6500 binary images. A -fold validation was invoked tocheck the validity of the classification which resulted in a classificationrate of 98.4% and 98.9% being achieved for these two databases. Thisframework is distinguished by its invariance to in-plane rotation of theimages under consideration. Experiments show that the classification rateremains almost constant when traffic sign images with different angles ofrotations were tested. Walter de Gruyter Online Zeitschriften Pattern recognition Classification Boosting Traffic Sign Recognition Davami, Erfan verfasserin aut Enthalten in Journal of intelligent systems Berlin : de Gruyter, 1992 21(2012), 1 vom: 29. Feb., Seite 25-43 (DE-627)NLEJ248236105 (DE-600)2598392-1 2191-026X nnns volume:21 year:2012 number:1 day:29 month:02 pages:25-43 extent:19 https://doi.org/10.1515/jisys-2012-0001 Deutschlandweit zugänglich GBV_USEFLAG_U ZDB-1-DGR GBV_NL_ARTICLE AR 21 2012 1 29 02 25-43 19 |
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10.1515/jisys-2012-0001 doi artikel_Grundlieferung.pp (DE-627)NLEJ247092363 DE-627 ger DE-627 rakwb Fleyeh, Hasan verfasserin aut Classification with NormalBoost: Case Study Traffic Sign Classification Walter de Gruyter GmbH & Co. KG 2012 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier NormalBoost is a new boosting algorithm which is capable of classifying amulti-dimensional binary class dataset. It adaptively combines several weakclassifiers to form a strong classifier. Unlike many boosting algorithmswhich have high computation and memory complexities, NormalBoost is capableof classification with low complexity.The purpose of this paper is to present NormalBoost as a framework whichestablishes a platform to solve classification problems. The approach wastested with a dataset which was extracted automatically from real-worldtraffic sign images. The dataset contains both images of traffic signborders and speed limit pictograms. This framework involves the computationof Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images whichwere classified was 6500 binary images. A -fold validation was invoked tocheck the validity of the classification which resulted in a classificationrate of 98.4% and 98.9% being achieved for these two databases. Thisframework is distinguished by its invariance to in-plane rotation of theimages under consideration. Experiments show that the classification rateremains almost constant when traffic sign images with different angles ofrotations were tested. Walter de Gruyter Online Zeitschriften Pattern recognition Classification Boosting Traffic Sign Recognition Davami, Erfan verfasserin aut Enthalten in Journal of intelligent systems Berlin : de Gruyter, 1992 21(2012), 1 vom: 29. Feb., Seite 25-43 (DE-627)NLEJ248236105 (DE-600)2598392-1 2191-026X nnns volume:21 year:2012 number:1 day:29 month:02 pages:25-43 extent:19 https://doi.org/10.1515/jisys-2012-0001 Deutschlandweit zugänglich GBV_USEFLAG_U ZDB-1-DGR GBV_NL_ARTICLE AR 21 2012 1 29 02 25-43 19 |
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10.1515/jisys-2012-0001 doi artikel_Grundlieferung.pp (DE-627)NLEJ247092363 DE-627 ger DE-627 rakwb Fleyeh, Hasan verfasserin aut Classification with NormalBoost: Case Study Traffic Sign Classification Walter de Gruyter GmbH & Co. KG 2012 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier NormalBoost is a new boosting algorithm which is capable of classifying amulti-dimensional binary class dataset. It adaptively combines several weakclassifiers to form a strong classifier. Unlike many boosting algorithmswhich have high computation and memory complexities, NormalBoost is capableof classification with low complexity.The purpose of this paper is to present NormalBoost as a framework whichestablishes a platform to solve classification problems. The approach wastested with a dataset which was extracted automatically from real-worldtraffic sign images. The dataset contains both images of traffic signborders and speed limit pictograms. This framework involves the computationof Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images whichwere classified was 6500 binary images. A -fold validation was invoked tocheck the validity of the classification which resulted in a classificationrate of 98.4% and 98.9% being achieved for these two databases. Thisframework is distinguished by its invariance to in-plane rotation of theimages under consideration. Experiments show that the classification rateremains almost constant when traffic sign images with different angles ofrotations were tested. Walter de Gruyter Online Zeitschriften Pattern recognition Classification Boosting Traffic Sign Recognition Davami, Erfan verfasserin aut Enthalten in Journal of intelligent systems Berlin : de Gruyter, 1992 21(2012), 1 vom: 29. Feb., Seite 25-43 (DE-627)NLEJ248236105 (DE-600)2598392-1 2191-026X nnns volume:21 year:2012 number:1 day:29 month:02 pages:25-43 extent:19 https://doi.org/10.1515/jisys-2012-0001 Deutschlandweit zugänglich GBV_USEFLAG_U ZDB-1-DGR GBV_NL_ARTICLE AR 21 2012 1 29 02 25-43 19 |
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10.1515/jisys-2012-0001 doi artikel_Grundlieferung.pp (DE-627)NLEJ247092363 DE-627 ger DE-627 rakwb Fleyeh, Hasan verfasserin aut Classification with NormalBoost: Case Study Traffic Sign Classification Walter de Gruyter GmbH & Co. KG 2012 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier NormalBoost is a new boosting algorithm which is capable of classifying amulti-dimensional binary class dataset. It adaptively combines several weakclassifiers to form a strong classifier. Unlike many boosting algorithmswhich have high computation and memory complexities, NormalBoost is capableof classification with low complexity.The purpose of this paper is to present NormalBoost as a framework whichestablishes a platform to solve classification problems. The approach wastested with a dataset which was extracted automatically from real-worldtraffic sign images. The dataset contains both images of traffic signborders and speed limit pictograms. This framework involves the computationof Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images whichwere classified was 6500 binary images. A -fold validation was invoked tocheck the validity of the classification which resulted in a classificationrate of 98.4% and 98.9% being achieved for these two databases. Thisframework is distinguished by its invariance to in-plane rotation of theimages under consideration. Experiments show that the classification rateremains almost constant when traffic sign images with different angles ofrotations were tested. Walter de Gruyter Online Zeitschriften Pattern recognition Classification Boosting Traffic Sign Recognition Davami, Erfan verfasserin aut Enthalten in Journal of intelligent systems Berlin : de Gruyter, 1992 21(2012), 1 vom: 29. Feb., Seite 25-43 (DE-627)NLEJ248236105 (DE-600)2598392-1 2191-026X nnns volume:21 year:2012 number:1 day:29 month:02 pages:25-43 extent:19 https://doi.org/10.1515/jisys-2012-0001 Deutschlandweit zugänglich GBV_USEFLAG_U ZDB-1-DGR GBV_NL_ARTICLE AR 21 2012 1 29 02 25-43 19 |
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10.1515/jisys-2012-0001 doi artikel_Grundlieferung.pp (DE-627)NLEJ247092363 DE-627 ger DE-627 rakwb Fleyeh, Hasan verfasserin aut Classification with NormalBoost: Case Study Traffic Sign Classification Walter de Gruyter GmbH & Co. KG 2012 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier NormalBoost is a new boosting algorithm which is capable of classifying amulti-dimensional binary class dataset. It adaptively combines several weakclassifiers to form a strong classifier. Unlike many boosting algorithmswhich have high computation and memory complexities, NormalBoost is capableof classification with low complexity.The purpose of this paper is to present NormalBoost as a framework whichestablishes a platform to solve classification problems. The approach wastested with a dataset which was extracted automatically from real-worldtraffic sign images. The dataset contains both images of traffic signborders and speed limit pictograms. This framework involves the computationof Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images whichwere classified was 6500 binary images. A -fold validation was invoked tocheck the validity of the classification which resulted in a classificationrate of 98.4% and 98.9% being achieved for these two databases. Thisframework is distinguished by its invariance to in-plane rotation of theimages under consideration. Experiments show that the classification rateremains almost constant when traffic sign images with different angles ofrotations were tested. Walter de Gruyter Online Zeitschriften Pattern recognition Classification Boosting Traffic Sign Recognition Davami, Erfan verfasserin aut Enthalten in Journal of intelligent systems Berlin : de Gruyter, 1992 21(2012), 1 vom: 29. Feb., Seite 25-43 (DE-627)NLEJ248236105 (DE-600)2598392-1 2191-026X nnns volume:21 year:2012 number:1 day:29 month:02 pages:25-43 extent:19 https://doi.org/10.1515/jisys-2012-0001 Deutschlandweit zugänglich GBV_USEFLAG_U ZDB-1-DGR GBV_NL_ARTICLE AR 21 2012 1 29 02 25-43 19 |
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NormalBoost is a new boosting algorithm which is capable of classifying amulti-dimensional binary class dataset. It adaptively combines several weakclassifiers to form a strong classifier. Unlike many boosting algorithmswhich have high computation and memory complexities, NormalBoost is capableof classification with low complexity.The purpose of this paper is to present NormalBoost as a framework whichestablishes a platform to solve classification problems. The approach wastested with a dataset which was extracted automatically from real-worldtraffic sign images. The dataset contains both images of traffic signborders and speed limit pictograms. This framework involves the computationof Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images whichwere classified was 6500 binary images. A -fold validation was invoked tocheck the validity of the classification which resulted in a classificationrate of 98.4% and 98.9% being achieved for these two databases. Thisframework is distinguished by its invariance to in-plane rotation of theimages under consideration. Experiments show that the classification rateremains almost constant when traffic sign images with different angles ofrotations were tested. |
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NormalBoost is a new boosting algorithm which is capable of classifying amulti-dimensional binary class dataset. It adaptively combines several weakclassifiers to form a strong classifier. Unlike many boosting algorithmswhich have high computation and memory complexities, NormalBoost is capableof classification with low complexity.The purpose of this paper is to present NormalBoost as a framework whichestablishes a platform to solve classification problems. The approach wastested with a dataset which was extracted automatically from real-worldtraffic sign images. The dataset contains both images of traffic signborders and speed limit pictograms. This framework involves the computationof Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images whichwere classified was 6500 binary images. A -fold validation was invoked tocheck the validity of the classification which resulted in a classificationrate of 98.4% and 98.9% being achieved for these two databases. Thisframework is distinguished by its invariance to in-plane rotation of theimages under consideration. Experiments show that the classification rateremains almost constant when traffic sign images with different angles ofrotations were tested. |
| abstract_unstemmed |
NormalBoost is a new boosting algorithm which is capable of classifying amulti-dimensional binary class dataset. It adaptively combines several weakclassifiers to form a strong classifier. Unlike many boosting algorithmswhich have high computation and memory complexities, NormalBoost is capableof classification with low complexity.The purpose of this paper is to present NormalBoost as a framework whichestablishes a platform to solve classification problems. The approach wastested with a dataset which was extracted automatically from real-worldtraffic sign images. The dataset contains both images of traffic signborders and speed limit pictograms. This framework involves the computationof Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images whichwere classified was 6500 binary images. A -fold validation was invoked tocheck the validity of the classification which resulted in a classificationrate of 98.4% and 98.9% being achieved for these two databases. Thisframework is distinguished by its invariance to in-plane rotation of theimages under consideration. Experiments show that the classification rateremains almost constant when traffic sign images with different angles ofrotations were tested. |
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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">NLEJ247092363</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220820030154.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220814s2012 xx |||||o 00| ||und c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1515/jisys-2012-0001</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">artikel_Grundlieferung.pp</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ247092363</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="100" ind1="1" ind2=" "><subfield code="a">Fleyeh, Hasan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Classification with NormalBoost: Case Study Traffic Sign Classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="b">Walter de Gruyter GmbH & Co. KG</subfield><subfield code="c">2012</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">19</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">NormalBoost is a new boosting algorithm which is capable of classifying amulti-dimensional binary class dataset. It adaptively combines several weakclassifiers to form a strong classifier. Unlike many boosting algorithmswhich have high computation and memory complexities, NormalBoost is capableof classification with low complexity.The purpose of this paper is to present NormalBoost as a framework whichestablishes a platform to solve classification problems. The approach wastested with a dataset which was extracted automatically from real-worldtraffic sign images. The dataset contains both images of traffic signborders and speed limit pictograms. This framework involves the computationof Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images whichwere classified was 6500 binary images. A -fold validation was invoked tocheck the validity of the classification which resulted in a classificationrate of 98.4% and 98.9% being achieved for these two databases. Thisframework is distinguished by its invariance to in-plane rotation of theimages under consideration. Experiments show that the classification rateremains almost constant when traffic sign images with different angles ofrotations were tested.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="f">Walter de Gruyter Online Zeitschriften</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pattern recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Boosting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Traffic Sign Recognition</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Davami, Erfan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of intelligent systems</subfield><subfield code="d">Berlin : de Gruyter, 1992</subfield><subfield code="g">21(2012), 1 vom: 29. 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