Feature weighting via optimal thresholding for video analysis

Xu, Z; Yang, Y; Tsang, I; Sebe, N; Hauptmann, AG

Feature weighting via optimal thresholding for video analysis

Xu, Z Yang, Y

Tsang, I

Sebe, N Hauptmann, AG

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 3440 - 3447
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Xu, Z	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Tsang, I https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Sebe, N	en_US
dc.contributor.author	Hauptmann, AG	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 3440 - 3447	en_US
dc.identifier.isbn	9781479928392	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29571
dc.description.abstract	Fusion of multiple features can boost the performance of large-scale visual classification and detection tasks like TRECVID Multimedia Event Detection (MED) competition [1]. In this paper, we propose a novel feature fusion approach, namely Feature Weighting via Optimal Thresholding (FWOT) to effectively fuse various features. FWOT learns the weights, thresholding and smoothing parameters in a joint framework to combine the decision values obtained from all the individual features and the early fusion. To the best of our knowledge, this is the first work to consider the weight and threshold factors of fusion problem simultaneously. Compared to state-of-the-art fusion algorithms, our approach achieves promising improvements on HMDB [8] action recognition dataset and CCV [5] video classification dataset. In addition, experiments on two TRECVID MED 2011 collections show that our approach outperforms the state-of-the-art fusion methods for complex event detection. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the IEEE International Conference on Computer Vision	en_US
dc.relation.isbasedon	10.1109/ICCV.2013.427	en_US
dc.title	Feature weighting via optimal thresholding for video analysis	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Fusion of multiple features can boost the performance of large-scale visual classification and detection tasks like TRECVID Multimedia Event Detection (MED) competition [1]. In this paper, we propose a novel feature fusion approach, namely Feature Weighting via Optimal Thresholding (FWOT) to effectively fuse various features. FWOT learns the weights, thresholding and smoothing parameters in a joint framework to combine the decision values obtained from all the individual features and the early fusion. To the best of our knowledge, this is the first work to consider the weight and threshold factors of fusion problem simultaneously. Compared to state-of-the-art fusion algorithms, our approach achieves promising improvements on HMDB [8] action recognition dataset and CCV [5] video classification dataset. In addition, experiments on two TRECVID MED 2011 collections show that our approach outperforms the state-of-the-art fusion methods for complex event detection. © 2013 IEEE.

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http://hdl.handle.net/10453/29571