A new method for shot gradual transiton detection using support vector machine

被引:0
|
作者
Ling, J [1 ]
Lian, YQ [1 ]
Zhuang, YT [1 ]
机构
[1] Zhejiang Univ, Inst Artificial Intelligence, Hangzhou 310027, Peoples R China
关键词
variance projection function; gradual transition detection; video similarity; support vector machine;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The detection of gradual transition is much more difficult than that of abrupt transition. In this paper, a new method for gradual transition detection that applies support vector machine is proposed. First, an improved variance projection function is introduced, and its practicality to the detection of gradual transition is analyzed as well. Then by using this variance projection function, the distance between the video frames is defined, and a method to calculate the feature vector of changes of the distance is proposed. Finally, a statistical learning method based on the support vector machine is devised to determine whether the changes of the distance are caused by gradual transition or not. The experiments results show that this method has better detection resolution and less timing complexity, and thus satisfactorily meets the requirements of real-time video-shot detection.
引用
收藏
页码:5599 / 5604
页数:6
相关论文
共 50 条
  • [21] Detection of Splice Sites Using Support Vector Machine
    Varadwaj, Pritish
    Purohit, Neetesh
    Arora, Bhumika
    CONTEMPORARY COMPUTING, PROCEEDINGS, 2009, 40 : 493 - 502
  • [22] Detection of Microcalcifications in Mammograms Using Support Vector Machine
    Sharkas, Maha
    Al-Sharkawy, Mohamed
    Ragab, Dina Ahmed
    UKSIM FIFTH EUROPEAN MODELLING SYMPOSIUM ON COMPUTER MODELLING AND SIMULATION (EMS 2011), 2011, : 179 - 184
  • [23] Sensor fault detection using support vector machine
    Wang, Yonghua
    Li, Benwei
    Journal of Information and Computational Science, 2012, 9 (10): : 2915 - 2922
  • [24] Atrial Fibrillation Detection Using Support Vector Machine
    Nuryani, Nuryani
    Harjito, Bambang
    Yahya, Iwan
    Lestari, Anik
    PROCEEDING JOINT INTERNATIONAL CONFERENCE ON ELECTRIC VEHICULAR TECHNOLOGY AND INDUSTRIAL, MECHANICAL, ELECTRICAL, AND CHEMICAL ENGINEERING (ICEVT & IMECE), 2015, : 215 - 218
  • [25] Pathological Voices Detection using Support Vector Machine
    Hammami, Imen
    Salhi, Lotfi
    Labidi, Salam
    2016 2ND INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR SIGNAL AND IMAGE PROCESSING (ATSIP), 2016, : 662 - 666
  • [26] Intrusion Detection Using Isomap and Support Vector Machine
    Zheng, Kai-mei
    Qian, Xu
    Zhou, Yu
    Jia, Li-juan
    2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL III, PROCEEDINGS, 2009, : 235 - 239
  • [27] Weed Detection System using Support Vector Machine
    Ishak, Asnor Juraiza
    Mustafa, Mohd Marzuki
    Tahir, Noritawati Md
    Hussain, Aini
    2008 INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY AND ITS APPLICATIONS, VOLS 1-3, 2008, : 445 - 448
  • [28] Bank Fraud Detection Using Support Vector Machine
    Gyamfi, Nana Kwame
    Abdulai, Jamal-Deen
    2018 IEEE 9TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), 2018, : 37 - 41
  • [29] Fraud detection using support vector machine ensemble
    Pang, SN
    Kim, D
    Bang, SY
    8TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, VOLS 1-3, PROCEEDING, 2001, : 1344 - 1349
  • [30] A gradual training algorithm of incremental support vector machine learning
    Zhang, JP
    Li, ZW
    Yang, J
    Li, Y
    ADVANCES IN NATURAL COMPUTATION, PT 1, PROCEEDINGS, 2005, 3610 : 1132 - 1139