Paper Info
Title
Efficient video copy detection via aligning video signature time series
Abstract
Various methods of content-based video copy detection have been proposed to find video copies in a large video database. In this paper, we represent video feature obtained by global and/or local detectors as signature time series. We observe that the curves of such time series under various kinds of modifications and transformations follow similar trends. Based on this observation, we propose to use linear segmentation to approximate the time series and extract major inclines from those linear segments. We develop a major incline-based fast alignment method to find potential alignment positions between the compared videos. Further, taking advantage of the major incline-based fast alignment, a Frame Insertion, Deletion, and Substitutions (FIDS) detection method is introduced to detect video copies in the presence of frame order changes. Our proposed solution is simple and generic. It can be combined with existing global or local feature descriptions, and with sequence or keyframe based matching schemes. It speeds up the video copy detection process by reducing the search space to the areas suggested by the potential alignments. Experiments using both the MUSCLE VCD 2007 and TRECVID CBCD 2009 datasets show that the proposed solution significantly accelerates the overall video copy detection process and at the same time achieves good precision.
Year
DOI
Venue
2012
10.1145/2324796.2324814
ICMR
Keywords
Field
DocType
content-based video copy detection,video signature time series,time series,video feature,major incline-based fast alignment,efficient video copy detection,detection method,proposed solution,video copy,video copy detection process,large video database,overall video copy detection,sequence alignment,search space
Computer vision,Block-matching algorithm,Pattern recognition,Similarity measure,TRECVID,Computer science,Segmentation,Multiview Video Coding,Video tracking,Video copy detection,Artificial intelligence,Detector
Conference
Citations 
PageRank 
References 
18
0.69
20
Authors
4
Name
Order
Citations
PageRank
Jennifer Ren1180.69
Fangzhe Chang224815.32
Thomas Wood3211.53
John R. Zhang4584.90