Using detected visual objects to index video database

Du, X; Yin, H; Huang, Z; Yang, Y; Zhou, X

Using detected visual objects to index video database

Du, X Yin, H Huang, Z Yang, Y

Zhou, X

Permalink

Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9877 LNCS pp. 333 - 345
Issue Date:: 2016-01-01

Closed Access

	Filename	Description	Size
	Published paper.pdf	Published version	238.87 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	Du, X	en_US
dc.contributor.author	Yin, H	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	Zhou, X	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9877 LNCS pp. 333 - 345	en_US
dc.identifier.isbn	9783319469218	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/100166
dc.description.abstract	© Springer International Publishing AG 2016. In this paper, we focus on how to use visual objects to index the videos. Two tables are constructed for this purpose, namely the unique object table and the occurrence table. The former table stores the unique objects which appear in the videos, while the latter table stores the occurrence information of these unique objects in the videos. In previous works, these two tables are generated manually by a topdown process. That is, the unique object table is given by the experts at first, then the occurrence table is generated by the annotators according to the unique object table. Obviously, such process which heavily depends on human labors limits the scalability especially when the data are dynamic or large-scale. To improve this, we propose to perform a bottom-up process to generate these two tables. The novelties are: we use object detector instead of human annotation to create the occurrence table; we propose a hybrid method which consists of local merge, global merge and propagation to generate the unique object table and fix the occurrence table. In fact, there are another three candidate methods for implementing the bottom-up process, namely, recognizing-based, matching-based and tracking-based methods. Through analyzing their mechanism and evaluating their accuracy, we find that they are not suitable for the bottom-up process. The proposed hybrid method leverages the advantages of the matching-based and tracking-based methods. Our experiments show that the hybrid method is more accurate and efficient than the candidate methods, which indicates that it is more suitable for the proposed bottom-up process.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150103008
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-46922-5_26	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Using detected visual objects to index video database	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9877 LNCS	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	closed_access
pubs.publication-status	Published	en_US
pubs.volume	9877 LNCS	en_US

Abstract:

© Springer International Publishing AG 2016. In this paper, we focus on how to use visual objects to index the videos. Two tables are constructed for this purpose, namely the unique object table and the occurrence table. The former table stores the unique objects which appear in the videos, while the latter table stores the occurrence information of these unique objects in the videos. In previous works, these two tables are generated manually by a topdown process. That is, the unique object table is given by the experts at first, then the occurrence table is generated by the annotators according to the unique object table. Obviously, such process which heavily depends on human labors limits the scalability especially when the data are dynamic or large-scale. To improve this, we propose to perform a bottom-up process to generate these two tables. The novelties are: we use object detector instead of human annotation to create the occurrence table; we propose a hybrid method which consists of local merge, global merge and propagation to generate the unique object table and fix the occurrence table. In fact, there are another three candidate methods for implementing the bottom-up process, namely, recognizing-based, matching-based and tracking-based methods. Through analyzing their mechanism and evaluating their accuracy, we find that they are not suitable for the bottom-up process. The proposed hybrid method leverages the advantages of the matching-based and tracking-based methods. Our experiments show that the hybrid method is more accurate and efficient than the candidate methods, which indicates that it is more suitable for the proposed bottom-up process.

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

http://hdl.handle.net/10453/100166