ViBE: A Compressed Video Database Structured for Active Browsing and Search

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Author

J Chen, C Taskiran, A Albiol, E Delp, C Bouman

Tech report number

CERIAS TR 2004-117

Entry type

article

Abstract

In this paper, we describe a unique new paradigm for video database management known as ViBE (Video Indexing and Browsing Environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. The system first segments video sequences into shots by using a new feature vector known as the Generalized Trace obtained from the DC-sequence of the compressed data. Each video shot is then represented by a hierarchical structure known as the shot tree. The shots are then classified into pseudo-semantic classes that describe the shot content. Finally, the results are presented to the user in an active browsing environment using a similarity pyramid data structure. The similarity pyramid allows the user to view the video database at various levels of detail. The user can also define semantic classes and reorganize the browsing environment based on relevance feedback. We describe how ViBE performs on a database of MPEG sequences.

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Date

2004 – 02

URL

ftp://skynet.ecn.purdue.edu/pub/dist/delp/vibe-ip/paper.pdf

Journal

IEEE Transactions on Multimedia

Key alpha

Delp

Number

Pages

103-118

Volume

Publication Date

2004-02-00

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@Article{ Delp,
	title = "ViBE: A Compressed Video Database Structured for Active Browsing and Search",
	author = "J Chen, C Taskiran, A Albiol, E Delp, C Bouman",
	year = "2004",
	month = "02",
	journal = "IEEE Transactions on Multimedia",
	number = "1",
	pages = "103-118",
	volume = "6",
	abstract = "In this paper, we describe a unique new paradigm for video database management known as ViBE (Video Indexing and Browsing Environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. The system first segments video sequences into shots by using a new feature vector known as the Generalized Trace obtained from 
the DC-sequence of the compressed data. Each video shot is then represented by a hierarchical 
structure known as the shot tree. The shots are then classified into pseudo-semantic classes that 
describe the shot content. Finally, the results are presented to the user in an active browsing environment using a similarity pyramid data structure. The similarity pyramid allows the user to view the video database at various levels of detail. The user can also define semantic classes and reorganize the browsing environment based on relevance feedback. We describe how ViBE 
performs on a database of MPEG sequences. ",
	url = "ftp://skynet.ecn.purdue.edu/pub/dist/delp/vibe-ip/paper.pdf",
}