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
  • [1] A Support Vector Machine Approach for Video Shot Detection
    Chasanis, Vasileios
    Likas, Aristidis
    Calatsanos, Nikolaos
    NEW DIRECTIONS IN INTELLIGENT INTERACTIVE MULTIMEDIA, 2008, 142 : 45 - 54
  • [2] DWT-based Shot Boundary Detection Using Support Vector Machine
    Li, Jun
    Ding, Youdong
    Shi, Yunyu
    Zeng, Qingyue
    FIFTH INTERNATIONAL CONFERENCE ON INFORMATION ASSURANCE AND SECURITY, VOL 1, PROCEEDINGS, 2009, : 435 - 438
  • [3] A robust shot transition detection method based on support vector machine in compressed domain
    Cao, Jianrong
    Cai, Anni
    PATTERN RECOGNITION LETTERS, 2007, 28 (12) : 1534 - 1540
  • [4] A Novel Smoke Detection Method Using Support Vector Machine
    Maruta, Hidenori
    Nakamura, Akihiro
    Kurokawa, Fujio
    TENCON 2010: 2010 IEEE REGION 10 CONFERENCE, 2010, : 210 - 215
  • [5] A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection
    Liu, Wei
    Ci, LinLin
    Liu, LiPing
    APPLIED SCIENCES-BASEL, 2020, 10 (03):
  • [6] A new method for mispronunciation detection using Support Vector Machine based on Pronunciation Space Models
    Wei, Si
    Hu, Guoping
    Hu, Yu
    Wang, Ren-Hua
    SPEECH COMMUNICATION, 2009, 51 (10) : 896 - 905
  • [7] Adaptive Magnetic Anomaly Detection Method Using Support Vector Machine
    Fan, Liming
    Kang, Chong
    Wang, Huigang
    Hu, Hao
    Zhang, Xiaojun
    Liu, Xing
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [9] A New Efficient Image Edge Detection Method Based on Support Vector Machine
    Wu Peng
    Chen Qichao
    2010 INTERNATIONAL CONFERENCE ON COMMUNICATION AND VEHICULAR TECHNOLOGY (ICCVT 2010), VOL II, 2010, : 305 - 309
  • [10] Algorithm for shot boundary detection based on support vector machine in compressed domain
    Shandong Jianzhu University, Jinan 250101, China
    不详
    Tien Tzu Hsueh Pao, 2008, 1 (203-208):