Semi-Supervised Multimodal Fusion Model for Social Event Detection on Web Image Collections
In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Qing [verfasserIn] Yang, Zhenguo [verfasserIn] Lu, Zheng [verfasserIn] Ma, Yun [verfasserIn] Gong, Zhiguo [verfasserIn] Pan, Haiwei [verfasserIn] Chen, Yangbin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Umfang: |
1 Online-Ressource |
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| Übergeordnetes Werk: |
Enthalten in: International journal of multimedia data engineering and management - Hershey, Pa : IGI Global, 2010, 6(2015), 4, Seite 1-22 |
|---|---|
| Übergeordnetes Werk: |
volume:6 ; year:2015 ; number:4 ; pages:1-22 |
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| DOI / URN: |
10.4018/IJMDEM.2015100101 |
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NLEJ251820580 |
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| 520 | |a In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous features seamlessly and efficiently. Particularly, a self-tuning approach is proposed to tune the parameters in the process of feature reinforcement automatically. Furthermore, to deal with missing values in raw features, prior knowledge is utilized to estimate the missing ones as a preprocessing step, and SMF will further extend an extra attribute to indicate if the values in the fused feature are missing. Based on the fused expression achieved by SMF, a series of algorithms are designed by adopting clustering and classification strategies separately. Extensive experiments conducted on the MediaEval social event detection challenge reveal that SMF-based approaches outperform the baselines | ||
| 653 | |a Event Detection |a Feature Representation |a Multimedia Data Mining |a Multimodal Fusion |a Semi-supervised Learning |a Social Media Analysis | ||
| 700 | 1 | |a Yang, Zhenguo |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Zheng |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Gong, Zhiguo |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Haiwei |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yangbin |e verfasserin |4 aut | |
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10.4018/IJMDEM.2015100101 doi (DE-627)NLEJ251820580 (VZGNL)10.4018/IJMDEM.2015100101 DE-627 ger DE-627 rakwb eng Li, Qing verfasserin aut Semi-Supervised Multimodal Fusion Model for Social Event Detection on Web Image Collections 2015 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous features seamlessly and efficiently. Particularly, a self-tuning approach is proposed to tune the parameters in the process of feature reinforcement automatically. Furthermore, to deal with missing values in raw features, prior knowledge is utilized to estimate the missing ones as a preprocessing step, and SMF will further extend an extra attribute to indicate if the values in the fused feature are missing. Based on the fused expression achieved by SMF, a series of algorithms are designed by adopting clustering and classification strategies separately. Extensive experiments conducted on the MediaEval social event detection challenge reveal that SMF-based approaches outperform the baselines Event Detection Feature Representation Multimedia Data Mining Multimodal Fusion Semi-supervised Learning Social Media Analysis Yang, Zhenguo verfasserin aut Lu, Zheng verfasserin aut Ma, Yun verfasserin aut Gong, Zhiguo verfasserin aut Pan, Haiwei verfasserin aut Chen, Yangbin verfasserin aut Enthalten in International journal of multimedia data engineering and management Hershey, Pa : IGI Global, 2010 6(2015), 4, Seite 1-22 Online-Ressource (DE-627)NLEJ244419310 (DE-600)2703562-1 1947-8542 nnns volume:6 year:2015 number:4 pages:1-22 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 6 2015 4 1-22 |
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10.4018/IJMDEM.2015100101 doi (DE-627)NLEJ251820580 (VZGNL)10.4018/IJMDEM.2015100101 DE-627 ger DE-627 rakwb eng Li, Qing verfasserin aut Semi-Supervised Multimodal Fusion Model for Social Event Detection on Web Image Collections 2015 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous features seamlessly and efficiently. Particularly, a self-tuning approach is proposed to tune the parameters in the process of feature reinforcement automatically. Furthermore, to deal with missing values in raw features, prior knowledge is utilized to estimate the missing ones as a preprocessing step, and SMF will further extend an extra attribute to indicate if the values in the fused feature are missing. Based on the fused expression achieved by SMF, a series of algorithms are designed by adopting clustering and classification strategies separately. Extensive experiments conducted on the MediaEval social event detection challenge reveal that SMF-based approaches outperform the baselines Event Detection Feature Representation Multimedia Data Mining Multimodal Fusion Semi-supervised Learning Social Media Analysis Yang, Zhenguo verfasserin aut Lu, Zheng verfasserin aut Ma, Yun verfasserin aut Gong, Zhiguo verfasserin aut Pan, Haiwei verfasserin aut Chen, Yangbin verfasserin aut Enthalten in International journal of multimedia data engineering and management Hershey, Pa : IGI Global, 2010 6(2015), 4, Seite 1-22 Online-Ressource (DE-627)NLEJ244419310 (DE-600)2703562-1 1947-8542 nnns volume:6 year:2015 number:4 pages:1-22 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 6 2015 4 1-22 |
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10.4018/IJMDEM.2015100101 doi (DE-627)NLEJ251820580 (VZGNL)10.4018/IJMDEM.2015100101 DE-627 ger DE-627 rakwb eng Li, Qing verfasserin aut Semi-Supervised Multimodal Fusion Model for Social Event Detection on Web Image Collections 2015 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous features seamlessly and efficiently. Particularly, a self-tuning approach is proposed to tune the parameters in the process of feature reinforcement automatically. Furthermore, to deal with missing values in raw features, prior knowledge is utilized to estimate the missing ones as a preprocessing step, and SMF will further extend an extra attribute to indicate if the values in the fused feature are missing. Based on the fused expression achieved by SMF, a series of algorithms are designed by adopting clustering and classification strategies separately. Extensive experiments conducted on the MediaEval social event detection challenge reveal that SMF-based approaches outperform the baselines Event Detection Feature Representation Multimedia Data Mining Multimodal Fusion Semi-supervised Learning Social Media Analysis Yang, Zhenguo verfasserin aut Lu, Zheng verfasserin aut Ma, Yun verfasserin aut Gong, Zhiguo verfasserin aut Pan, Haiwei verfasserin aut Chen, Yangbin verfasserin aut Enthalten in International journal of multimedia data engineering and management Hershey, Pa : IGI Global, 2010 6(2015), 4, Seite 1-22 Online-Ressource (DE-627)NLEJ244419310 (DE-600)2703562-1 1947-8542 nnns volume:6 year:2015 number:4 pages:1-22 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 6 2015 4 1-22 |
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10.4018/IJMDEM.2015100101 doi (DE-627)NLEJ251820580 (VZGNL)10.4018/IJMDEM.2015100101 DE-627 ger DE-627 rakwb eng Li, Qing verfasserin aut Semi-Supervised Multimodal Fusion Model for Social Event Detection on Web Image Collections 2015 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous features seamlessly and efficiently. Particularly, a self-tuning approach is proposed to tune the parameters in the process of feature reinforcement automatically. Furthermore, to deal with missing values in raw features, prior knowledge is utilized to estimate the missing ones as a preprocessing step, and SMF will further extend an extra attribute to indicate if the values in the fused feature are missing. Based on the fused expression achieved by SMF, a series of algorithms are designed by adopting clustering and classification strategies separately. Extensive experiments conducted on the MediaEval social event detection challenge reveal that SMF-based approaches outperform the baselines Event Detection Feature Representation Multimedia Data Mining Multimodal Fusion Semi-supervised Learning Social Media Analysis Yang, Zhenguo verfasserin aut Lu, Zheng verfasserin aut Ma, Yun verfasserin aut Gong, Zhiguo verfasserin aut Pan, Haiwei verfasserin aut Chen, Yangbin verfasserin aut Enthalten in International journal of multimedia data engineering and management Hershey, Pa : IGI Global, 2010 6(2015), 4, Seite 1-22 Online-Ressource (DE-627)NLEJ244419310 (DE-600)2703562-1 1947-8542 nnns volume:6 year:2015 number:4 pages:1-22 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 6 2015 4 1-22 |
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10.4018/IJMDEM.2015100101 doi (DE-627)NLEJ251820580 (VZGNL)10.4018/IJMDEM.2015100101 DE-627 ger DE-627 rakwb eng Li, Qing verfasserin aut Semi-Supervised Multimodal Fusion Model for Social Event Detection on Web Image Collections 2015 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous features seamlessly and efficiently. Particularly, a self-tuning approach is proposed to tune the parameters in the process of feature reinforcement automatically. Furthermore, to deal with missing values in raw features, prior knowledge is utilized to estimate the missing ones as a preprocessing step, and SMF will further extend an extra attribute to indicate if the values in the fused feature are missing. Based on the fused expression achieved by SMF, a series of algorithms are designed by adopting clustering and classification strategies separately. Extensive experiments conducted on the MediaEval social event detection challenge reveal that SMF-based approaches outperform the baselines Event Detection Feature Representation Multimedia Data Mining Multimodal Fusion Semi-supervised Learning Social Media Analysis Yang, Zhenguo verfasserin aut Lu, Zheng verfasserin aut Ma, Yun verfasserin aut Gong, Zhiguo verfasserin aut Pan, Haiwei verfasserin aut Chen, Yangbin verfasserin aut Enthalten in International journal of multimedia data engineering and management Hershey, Pa : IGI Global, 2010 6(2015), 4, Seite 1-22 Online-Ressource (DE-627)NLEJ244419310 (DE-600)2703562-1 1947-8542 nnns volume:6 year:2015 number:4 pages:1-22 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 6 2015 4 1-22 |
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semi-supervised multimodal fusion model for social event detection on web image collections |
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Semi-Supervised Multimodal Fusion Model for Social Event Detection on Web Image Collections |
| abstract |
In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous features seamlessly and efficiently. Particularly, a self-tuning approach is proposed to tune the parameters in the process of feature reinforcement automatically. Furthermore, to deal with missing values in raw features, prior knowledge is utilized to estimate the missing ones as a preprocessing step, and SMF will further extend an extra attribute to indicate if the values in the fused feature are missing. Based on the fused expression achieved by SMF, a series of algorithms are designed by adopting clustering and classification strategies separately. Extensive experiments conducted on the MediaEval social event detection challenge reveal that SMF-based approaches outperform the baselines |
| abstractGer |
In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous features seamlessly and efficiently. Particularly, a self-tuning approach is proposed to tune the parameters in the process of feature reinforcement automatically. Furthermore, to deal with missing values in raw features, prior knowledge is utilized to estimate the missing ones as a preprocessing step, and SMF will further extend an extra attribute to indicate if the values in the fused feature are missing. Based on the fused expression achieved by SMF, a series of algorithms are designed by adopting clustering and classification strategies separately. Extensive experiments conducted on the MediaEval social event detection challenge reveal that SMF-based approaches outperform the baselines |
| abstract_unstemmed |
In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous features seamlessly and efficiently. Particularly, a self-tuning approach is proposed to tune the parameters in the process of feature reinforcement automatically. Furthermore, to deal with missing values in raw features, prior knowledge is utilized to estimate the missing ones as a preprocessing step, and SMF will further extend an extra attribute to indicate if the values in the fused feature are missing. Based on the fused expression achieved by SMF, a series of algorithms are designed by adopting clustering and classification strategies separately. Extensive experiments conducted on the MediaEval social event detection challenge reveal that SMF-based approaches outperform the baselines |
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| title_short |
Semi-Supervised Multimodal Fusion Model for Social Event Detection on Web Image Collections |
| url |
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2015100101&buylink=true |
| remote_bool |
true |
| author2 |
Yang, Zhenguo Lu, Zheng Ma, Yun Gong, Zhiguo Pan, Haiwei Chen, Yangbin |
| author2Str |
Yang, Zhenguo Lu, Zheng Ma, Yun Gong, Zhiguo Pan, Haiwei Chen, Yangbin |
| ppnlink |
NLEJ244419310 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.4018/IJMDEM.2015100101 |
| up_date |
2024-07-06T11:41:50.326Z |
| _version_ |
1803829756901195776 |
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|
| score |
7.400549 |