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

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 measure...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2012-08, Vol.22 (8), p.1174-1187
Main Authors: Hyun-seok Min, Jae Young Choi, De Neve, W., Yong Man Ro
Format: Article
Language:English
Subjects:
Adaptation models
Applied sciences
Clips
Collective knowledge
Cryptography
Distance measurement
Exact sciences and technology
Face
Feature extraction
image folksonomy
Information, signal and communications theory
near-duplicate video clip detection
Preserves
Quadratic forms
semantic concept detection
semantic distance
semantic video signature
Semantics
Shot
Signal and communications theory
Silicon
Similarity
Studies
Telecommunications and information theory
Transformations
video copy detection
Visualization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2012.2197080