Scene Change Detection Using a Local Detection Tree and Clustering in Ubiquitous Environment

  • Authors:

  • Shin Seong-Yoon;Lee Jong-Chan;Baek Seong-Eun;Kang Oh-Hyong;Shin Jung-Hoon;Rhee Yang-Won

  • Affiliations:

  • Dept. of Computer and Information Engineering, Kunsan Natl. Univ., Kunsan City, South Korea 573-701;Dept. of Computer and Information Engineering, Kunsan Natl. Univ., Kunsan City, South Korea 573-701;Dept. of Computer and Information Engineering, Kunsan Natl. Univ., Kunsan City, South Korea 573-701;Dept. of Computer and Information Engineering, Kunsan Natl. Univ., Kunsan City, South Korea 573-701;College of Engineering Division of Applied Systems Engineering, Chonbuk Natl. Univ., Jeonju City, South Korea 561-756;Dept. of Computer and Information Engineering, Kunsan Natl. Univ., Kunsan City, South Korea 573-701

  • Venue:
  • ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

Processing of video data is embossed very importantly in ubiquitous environment. This paper proposes a Scene Change Detection method using the local decision tree and clustering. The local decision tree detects cluster boundaries wherein local scenes occur, in such a way as to compare time similarity distributions among the difference values between detected scenes and their adjacent frames, and group an unbroken sequence of frames with similarities in difference value into a cluster unit. In other words, the local decision tree method is used to detect local scenes from a cluster segmentation unit.