Open AccessExtended Temporal Ordinal Measurement Using Spatially Normalized Mean for Video Copy Detection
Author(s) -
Lee HeungKyu,
Kim June
Publication year - 2010
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.10.0209.0485
Subject(s) - mathematics , feature (linguistics) , pattern recognition (psychology) , noise (video) , ordinal regression , matching (statistics) , artificial intelligence , distortion (music) , precision and recall , statistics , algorithm , computer science , image (mathematics) , amplifier , computer network , philosophy , linguistics , bandwidth (computing)
This letter proposes a robust feature extraction method using a spatially normalized mean for temporal ordinal measurement. Before computing a rank matrix from the mean values of non‐overlapped blocks, each block mean is normalized so that it obeys the invariance property against linear additive and subtractive noise effects and is insensitive against multiplied and divided noise effects. Then, the temporal ordinal measures of spatially normalized mean values are computed for the feature matching. The performance of the proposed method showed about 95% accuracy in both precision and recall rates on various distortion environments, which represents the 2.7% higher performance on average compared to the temporal ordinal measurement.