TY - GEN
T1 - Shot boundary detection using multi-instance incremental and decremental one-class support vector machine
AU - Lin, Hanhe
AU - Deng, Jeremiah D.
AU - Woodford, Brendon J.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016/4/12
Y1 - 2016/4/12
N2 - This paper presents a novel framework to detect shot boundaries based on the One-Class Support Vector Machine (OCSVM). Instead of comparing the difference between pair-wise consecutive frames at a specific time, we measure the divergence between two OCSVM classifiers, which are learnt from two contextual sets, i.e., immediate past set and immediate future set. To speed up the processing procedure, the two OCSVM classifiers are updated in an online fashion by our proposed multi-instance incremental and decremental one-class support vector machine algorithm. Our approach, which inherits the advantages of OCSVM, is robust to noises such as abrupt illumination changes and large object or camera movements, and capable of detecting gradual transitions as well. Experimental results on some benchmark datasets compare favorably with the state-of-the-art methods.
AB - This paper presents a novel framework to detect shot boundaries based on the One-Class Support Vector Machine (OCSVM). Instead of comparing the difference between pair-wise consecutive frames at a specific time, we measure the divergence between two OCSVM classifiers, which are learnt from two contextual sets, i.e., immediate past set and immediate future set. To speed up the processing procedure, the two OCSVM classifiers are updated in an online fashion by our proposed multi-instance incremental and decremental one-class support vector machine algorithm. Our approach, which inherits the advantages of OCSVM, is robust to noises such as abrupt illumination changes and large object or camera movements, and capable of detecting gradual transitions as well. Experimental results on some benchmark datasets compare favorably with the state-of-the-art methods.
KW - Kernel method
KW - One-class
KW - Online learning
KW - Shot boundary detection
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84964091585&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-31753-3_14
DO - 10.1007/978-3-319-31753-3_14
M3 - Conference contribution
AN - SCOPUS:84964091585
SN - 978-3-319-31752-6
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 165
EP - 176
BT - Advances in Knowledge Discovery and Data Mining
A2 - Bailey, James
A2 - Khan, Latifur
A2 - Washio, Takashi
A2 - Dobbie, Gill
A2 - Huang, Joshua Zhexue
A2 - Wang, Ruili
PB - Springer
CY - Cham
T2 - 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
Y2 - 19 April 2016 through 22 April 2016
ER -