Near-Duplicate Video Clip Detection Using Model-Free Semantic Concept Detection and Adaptive Semantic Distance Measurement

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2012-08
Language
English
Article Type
Article
Keywords

COPY DETECTION; IMAGE RETRIEVAL; ANNOTATION; SEARCH; SEQUENCES; FEATURES

Citation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, v.22, no.8, pp.1174 - 1187

ISSN
1051-8215
DOI
10.1109/TCSVT.2012.2197080
URI
http://hdl.handle.net/10203/102783
Appears in Collection
EE-Journal Papers(저널논문)
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