Event Oriented Dictionary Learning for Complex Event Detection

Yan, Y; Yang, Y; Meng, D; Liu, G; Tong, W; Hauptmann, AG; Sebe, N

Event Oriented Dictionary Learning for Complex Event Detection

Yan, Y Yang, Y

Meng, D Liu, G Tong, W Hauptmann, AG Sebe, N

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2015, 24 (6), pp. 1867 - 1878
Issue Date:: 2015-06-01

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Field	Value	Language
dc.contributor.author	Yan, Y	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Meng, D	en_US
dc.contributor.author	Liu, G	en_US
dc.contributor.author	Tong, W	en_US
dc.contributor.author	Hauptmann, AG	en_US
dc.contributor.author	Sebe, N	en_US
dc.date.issued	2015-06-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2015, 24 (6), pp. 1867 - 1878	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121575
dc.description.abstract	© 1992-2012 IEEE. Complex event detection is a retrieval task with the goal of finding videos of a particular event in a large-scale unconstrained Internet video archive, given example videos and text descriptions. Nowadays, different multimodal fusion schemes of low-level and high-level features are extensively investigated and evaluated for the complex event detection task. However, how to effectively select the high-level semantic meaningful concepts from a large pool to assist complex event detection is rarely studied in the literature. In this paper, we propose a novel strategy to automatically select semantic meaningful concepts for the event detection task based on both the events-kit text descriptions and the concepts high-level feature descriptions. Moreover, we introduce a novel event oriented dictionary representation based on the selected semantic concepts. Toward this goal, we leverage training images (frames) of selected concepts from the semantic indexing dataset with a pool of 346 concepts, into a novel supervised multitask ℓp-norm dictionary learning framework. Extensive experimental results on TRECVID multimedia event detection dataset demonstrate the efficacy of our proposed method.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2015.2413294	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Event Oriented Dictionary Learning for Complex Event Detection	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	24	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	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	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

© 1992-2012 IEEE. Complex event detection is a retrieval task with the goal of finding videos of a particular event in a large-scale unconstrained Internet video archive, given example videos and text descriptions. Nowadays, different multimodal fusion schemes of low-level and high-level features are extensively investigated and evaluated for the complex event detection task. However, how to effectively select the high-level semantic meaningful concepts from a large pool to assist complex event detection is rarely studied in the literature. In this paper, we propose a novel strategy to automatically select semantic meaningful concepts for the event detection task based on both the events-kit text descriptions and the concepts high-level feature descriptions. Moreover, we introduce a novel event oriented dictionary representation based on the selected semantic concepts. Toward this goal, we leverage training images (frames) of selected concepts from the semantic indexing dataset with a pool of 346 concepts, into a novel supervised multitask ℓp-norm dictionary learning framework. Extensive experimental results on TRECVID multimedia event detection dataset demonstrate the efficacy of our proposed method.

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http://hdl.handle.net/10453/121575