E-LAMP: Integration of innovative ideas for multimedia event detection

Tong, W; Yang, Y; Jiang, L; Yu, SI; Lan, Z; Ma, Z; Sze, W; Younessian, E; Hauptmann, AG

E-LAMP: Integration of innovative ideas for multimedia event detection

Tong, W Yang, Y

Jiang, L Yu, SI Lan, Z Ma, Z Sze, W Younessian, E Hauptmann, AG

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Publication Type:: Journal Article
Citation:: Machine Vision and Applications, 2014, 25 (1), pp. 5 - 15
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Tong, W	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Jiang, L	en_US
dc.contributor.author	Yu, SI	en_US
dc.contributor.author	Lan, Z	en_US
dc.contributor.author	Ma, Z	en_US
dc.contributor.author	Sze, W	en_US
dc.contributor.author	Younessian, E	en_US
dc.contributor.author	Hauptmann, AG	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Machine Vision and Applications, 2014, 25 (1), pp. 5 - 15	en_US
dc.identifier.issn	0932-8092	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115884
dc.description.abstract	Detecting multimedia events in web videos is an emerging hot research area in the fields of multimedia and computer vision. In this paper, we introduce the core methods and technologies of the framework we developed recently for our Event Labeling through Analytic Media Processing (E-LAMP) system to deal with different aspects of the overall problem of event detection. More specifically, we have developed efficient methods for feature extraction so that we are able to handle large collections of video data with thousands of hours of videos. Second, we represent the extracted raw features in a spatial bag-of-words model with more effective tilings such that the spatial layout information of different features and different events can be better captured, thus the overall detection performance can be improved. Third, different from widely used early and late fusion schemes, a novel algorithm is developed to learn a more robust and discriminative intermediate feature representation from multiple features so that better event models can be built upon it. Finally, to tackle the additional challenge of event detection with only very few positive exemplars, we have developed a novel algorithm which is able to effectively adapt the knowledge learnt from auxiliary sources to assist the event detection. Both our empirical results and the official evaluation results on TRECVID MED'11 and MED'12 demonstrate the excellent performance of the integration of these ideas. © 2013 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Machine Vision and Applications	en_US
dc.relation.isbasedon	10.1007/s00138-013-0529-6	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	E-LAMP: Integration of innovative ideas for multimedia event detection	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	25	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

Detecting multimedia events in web videos is an emerging hot research area in the fields of multimedia and computer vision. In this paper, we introduce the core methods and technologies of the framework we developed recently for our Event Labeling through Analytic Media Processing (E-LAMP) system to deal with different aspects of the overall problem of event detection. More specifically, we have developed efficient methods for feature extraction so that we are able to handle large collections of video data with thousands of hours of videos. Second, we represent the extracted raw features in a spatial bag-of-words model with more effective tilings such that the spatial layout information of different features and different events can be better captured, thus the overall detection performance can be improved. Third, different from widely used early and late fusion schemes, a novel algorithm is developed to learn a more robust and discriminative intermediate feature representation from multiple features so that better event models can be built upon it. Finally, to tackle the additional challenge of event detection with only very few positive exemplars, we have developed a novel algorithm which is able to effectively adapt the knowledge learnt from auxiliary sources to assist the event detection. Both our empirical results and the official evaluation results on TRECVID MED'11 and MED'12 demonstrate the excellent performance of the integration of these ideas. © 2013 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/115884