Paper Info
Title
Near-Duplicate Video Clip Detection Using Model-Free Semantic Concept Detection and Adaptive Semantic Distance Measurement
Abstract
Motivated by the observation that content transformations tend to preserve the semantic information conveyed by video clips, this paper introduces a novel technique for near-duplicate video clip (NDVC) detection, leveraging model-free semantic concept detection and adaptive semantic distance measurement. In particular, model-free semantic concept detection is realized by taking advantage of the collective knowledge in an image folksonomy (which is an unstructured collection of user-contributed images and tags), facilitating the use of an unrestricted concept vocabulary. Adaptive semantic distance measurement is realized by means of the signature quadratic form distance (SQFD), making it possible to flexibly measure the similarity between video shots that contain a varying number of semantic concepts, and where these semantic concepts may also differ in terms of relevance and nature. Experimental results obtained for the MIRFLICKR-25000 image set (used as a source of collective knowledge) and the TRECVID 2009 video set (used to create query and reference video clips) demonstrate that model-free semantic concept detection and SQFD can be successfully used for the purpose of identifying NDVCs.
Year
DOI
Venue
2012
10.1109/TCSVT.2012.2197080
IEEE Transactions on Circuits and Systems for Video Technology
Keywords
Field
DocType
collective knowledge,semantics,face,silicon,feature extraction,semantic distance,visualization,knowledge based systems
Semantic similarity,Computer vision,Semantic technology,Information retrieval,Computer science,TRECVID,Image retrieval,Video copy detection,Semantic grid,Artificial intelligence,Semantic computing,Semantic compression
Journal
Volume
Issue
ISSN
22
8
1051-8215
Citations 
PageRank 
References 
11
0.52
36
Authors
4
Name
Order
Citations
PageRank
Hyunseok Min1557.87
Jae Young Choi245940.10
Wesley De Neve352554.41
Yong Man Ro41192125.87