Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition
Abstract Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yang, Zhi-Xin [verfasserIn] |
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| Format: |
E-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: Cognitive Computation - New York, NY : Springer, 2009, 10(2018), 6 vom: 10. Okt., Seite 908-921 |
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| Übergeordnetes Werk: |
volume:10 ; year:2018 ; number:6 ; day:10 ; month:10 ; pages:908-921 |
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| DOI / URN: |
10.1007/s12559-018-9598-1 |
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| Katalog-ID: |
SPR026526166 |
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| 520 | |a Abstract Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods. | ||
| 650 | 4 | |a ELM auto-encoder |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Convolutional neural networks |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a 3D shape recognition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multi-view feature aggregation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Tang, Lulu |4 aut | |
| 700 | 1 | |a Zhang, Kun |4 aut | |
| 700 | 1 | |a Wong, Pak Kin |4 aut | |
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10.1007/s12559-018-9598-1 doi (DE-627)SPR026526166 (SPR)s12559-018-9598-1-e DE-627 ger DE-627 rakwb eng Yang, Zhi-Xin verfasserin (orcid)0000-0001-9151-7758 aut Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods. ELM auto-encoder (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 3D shape recognition (dpeaa)DE-He213 Multi-view feature aggregation (dpeaa)DE-He213 Tang, Lulu aut Zhang, Kun aut Wong, Pak Kin aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 10(2018), 6 vom: 10. Okt., Seite 908-921 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:10 year:2018 number:6 day:10 month:10 pages:908-921 https://dx.doi.org/10.1007/s12559-018-9598-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2018 6 10 10 908-921 |
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10.1007/s12559-018-9598-1 doi (DE-627)SPR026526166 (SPR)s12559-018-9598-1-e DE-627 ger DE-627 rakwb eng Yang, Zhi-Xin verfasserin (orcid)0000-0001-9151-7758 aut Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods. ELM auto-encoder (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 3D shape recognition (dpeaa)DE-He213 Multi-view feature aggregation (dpeaa)DE-He213 Tang, Lulu aut Zhang, Kun aut Wong, Pak Kin aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 10(2018), 6 vom: 10. Okt., Seite 908-921 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:10 year:2018 number:6 day:10 month:10 pages:908-921 https://dx.doi.org/10.1007/s12559-018-9598-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2018 6 10 10 908-921 |
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10.1007/s12559-018-9598-1 doi (DE-627)SPR026526166 (SPR)s12559-018-9598-1-e DE-627 ger DE-627 rakwb eng Yang, Zhi-Xin verfasserin (orcid)0000-0001-9151-7758 aut Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods. ELM auto-encoder (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 3D shape recognition (dpeaa)DE-He213 Multi-view feature aggregation (dpeaa)DE-He213 Tang, Lulu aut Zhang, Kun aut Wong, Pak Kin aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 10(2018), 6 vom: 10. Okt., Seite 908-921 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:10 year:2018 number:6 day:10 month:10 pages:908-921 https://dx.doi.org/10.1007/s12559-018-9598-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2018 6 10 10 908-921 |
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10.1007/s12559-018-9598-1 doi (DE-627)SPR026526166 (SPR)s12559-018-9598-1-e DE-627 ger DE-627 rakwb eng Yang, Zhi-Xin verfasserin (orcid)0000-0001-9151-7758 aut Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods. ELM auto-encoder (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 3D shape recognition (dpeaa)DE-He213 Multi-view feature aggregation (dpeaa)DE-He213 Tang, Lulu aut Zhang, Kun aut Wong, Pak Kin aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 10(2018), 6 vom: 10. Okt., Seite 908-921 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:10 year:2018 number:6 day:10 month:10 pages:908-921 https://dx.doi.org/10.1007/s12559-018-9598-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2018 6 10 10 908-921 |
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10.1007/s12559-018-9598-1 doi (DE-627)SPR026526166 (SPR)s12559-018-9598-1-e DE-627 ger DE-627 rakwb eng Yang, Zhi-Xin verfasserin (orcid)0000-0001-9151-7758 aut Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods. ELM auto-encoder (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 3D shape recognition (dpeaa)DE-He213 Multi-view feature aggregation (dpeaa)DE-He213 Tang, Lulu aut Zhang, Kun aut Wong, Pak Kin aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 10(2018), 6 vom: 10. Okt., Seite 908-921 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:10 year:2018 number:6 day:10 month:10 pages:908-921 https://dx.doi.org/10.1007/s12559-018-9598-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2018 6 10 10 908-921 |
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View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. 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Yang, Zhi-Xin |
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Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition ELM auto-encoder (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 3D shape recognition (dpeaa)DE-He213 Multi-view feature aggregation (dpeaa)DE-He213 |
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multi-view cnn feature aggregation with elm auto-encoder for 3d shape recognition |
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Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition |
| abstract |
Abstract Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition |
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https://dx.doi.org/10.1007/s12559-018-9598-1 |
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Tang, Lulu Zhang, Kun Wong, Pak Kin |
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10.1007/s12559-018-9598-1 |
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2024-07-03T21:19:20.658Z |
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| score |
7.400132 |