Complex event detection via event oriented dictionary learning

Yan, Y; Yang, Y; Shen, H; Meng, D; Liu, G; Hauptmann, A; Sebe, N

Complex event detection via event oriented dictionary learning

Yan, Y Yang, Y

Shen, H Meng, D Liu, G Hauptmann, A Sebe, N

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the National Conference on Artificial Intelligence, 2015, 5 pp. 3841 - 3847
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	Shen, H	en_US
dc.contributor.author	Meng, D	en_US
dc.contributor.author	Liu, G	en_US
dc.contributor.author	Hauptmann, A	en_US
dc.contributor.author	Sebe, N	en_US
dc.date.issued	2015-06-01	en_US
dc.identifier.citation	Proceedings of the National Conference on Artificial Intelligence, 2015, 5 pp. 3841 - 3847	en_US
dc.identifier.isbn	9781577357032	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121622
dc.description.abstract	© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Complex event detection is a retrieval task with the goal of finding videos of a particular event in a largescale 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 two novel strategies 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. Towards this goal, we leverage training samples of selected concepts from the Semantic Indexing (SIN) dataset with a pool of 346 concepts, into a novel supervised multitask dictionary learning framework. Extensive experimental results on TRECVID Multimedia Event Detection (MED) dataset demonstrate the efficacy of our proposed method.	en_US
dc.relation.ispartof	Proceedings of the National Conference on Artificial Intelligence	en_US
dc.title	Complex event detection via event oriented dictionary learning	en_US
dc.type	Conference Proceeding
utslib.citation.volume	5	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	open_access
pubs.publication-status	Published	en_US
pubs.volume	5	en_US

Abstract:

© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Complex event detection is a retrieval task with the goal of finding videos of a particular event in a largescale 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 two novel strategies 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. Towards this goal, we leverage training samples of selected concepts from the Semantic Indexing (SIN) dataset with a pool of 346 concepts, into a novel supervised multitask dictionary learning framework. Extensive experimental results on TRECVID Multimedia Event Detection (MED) dataset demonstrate the efficacy of our proposed method.

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http://hdl.handle.net/10453/121622