UQMSG experiments for TRECVID 2011

Shen, HT; Shao, J; Huang, Z; Yang, Y; Song, J; Liu, J; Zhu, X

UQMSG experiments for TRECVID 2011

Shen, HT Shao, J Huang, Z Yang, Y

Song, J Liu, J Zhu, X

Permalink

Publication Type:: Conference Proceeding
Citation:: 2011 TREC Video Retrieval Evaluation Notebook Papers, 2011
Issue Date:: 2011-01-01

Closed Access

	Filename	Description	Size
	uqmsg.pdf	Published version	115.71 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Shen, HT	en_US
dc.contributor.author	Shao, J	en_US
dc.contributor.author	Huang, Z	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Song, J	en_US
dc.contributor.author	Liu, J	en_US
dc.contributor.author	Zhu, X	en_US
dc.date.issued	2011-01-01	en_US
dc.identifier.citation	2011 TREC Video Retrieval Evaluation Notebook Papers, 2011	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119494
dc.description.abstract	This paper describes the experimental framework of the University of Queensland's Multimedia Search Group (UQMSG) at TRECVID 2011. We participated in two tasks this year, both for the first time. For the semantic indexing task, we submitted four lite runs: L_A_UQMSG1_1, L_A_UQMSG2_2, L_A_UQMSG3_3 and L_A_UQMSG4_4. They are all of training type A (actually we only used IACC.1.tv10.training data), but with different parameter settings in our keyframe-based Laplacian Joint Group Lasso (LJGL) algorithm with Local Binary Patterns (LBP) feature. For the content-based copy detection task, we submitted two runs: UQMSG.m.nofa.mfh and UQMSG.m.balanced.mfh. They used only the video modality information of keyframes and were both based on our Multiple Feature Hashing (MFH) algorithm that fuses local (LBP) and global (HSV) visual features, with different application profiles (reducing the false alarm rate v.s. balancing false alarms and misses). Due to time constraint, we were not able to improve the performance of our systems adequately on all the available training data this year for these tasks. Evaluation results suggest that more efforts need to be made to well tune system parameters. In addition, sophisticated techniques beyond applying keyframe-level semantic concept propagation and near-duplicate detection are required for achieving better performance in video tasks.	en_US
dc.relation.ispartof	2011 TREC Video Retrieval Evaluation Notebook Papers	en_US
dc.title	UQMSG experiments for TRECVID 2011	en_US
dc.type	Conference Proceeding
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/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper describes the experimental framework of the University of Queensland's Multimedia Search Group (UQMSG) at TRECVID 2011. We participated in two tasks this year, both for the first time. For the semantic indexing task, we submitted four lite runs: L_A_UQMSG1_1, L_A_UQMSG2_2, L_A_UQMSG3_3 and L_A_UQMSG4_4. They are all of training type A (actually we only used IACC.1.tv10.training data), but with different parameter settings in our keyframe-based Laplacian Joint Group Lasso (LJGL) algorithm with Local Binary Patterns (LBP) feature. For the content-based copy detection task, we submitted two runs: UQMSG.m.nofa.mfh and UQMSG.m.balanced.mfh. They used only the video modality information of keyframes and were both based on our Multiple Feature Hashing (MFH) algorithm that fuses local (LBP) and global (HSV) visual features, with different application profiles (reducing the false alarm rate v.s. balancing false alarms and misses). Due to time constraint, we were not able to improve the performance of our systems adequately on all the available training data this year for these tasks. Evaluation results suggest that more efforts need to be made to well tune system parameters. In addition, sophisticated techniques beyond applying keyframe-level semantic concept propagation and near-duplicate detection are required for achieving better performance in video tasks.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/119494