Graph partition model for robust temporal data segmentation

Authors:
Jinhui Yuan;Bo Zhang;Fuzong Lin
Affiliations:
State Key Laboratory of Intelligent Technology and System, Department of Computer Science and Technology, Tsinghua University, Beijing, P.R.China;State Key Laboratory of Intelligent Technology and System, Department of Computer Science and Technology, Tsinghua University, Beijing, P.R.China;State Key Laboratory of Intelligent Technology and System, Department of Computer Science and Technology, Tsinghua University, Beijing, P.R.China
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

Citing 4
Cited 3

Information Retrieval

Information Retrieval
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
TRECVID: evaluating the effectiveness of information retrieval tasks on digital video

Proceedings of the 12th annual ACM international conference on Multimedia
Rapid scene analysis on compressed video

IEEE Transactions on Circuits and Systems for Video Technology

A unified shot boundary detection framework based on graph partition model

Proceedings of the 13th annual ACM international conference on Multimedia
Video scene segmentation and semantic representation using a novel scheme

Multimedia Tools and Applications
Mobile video browsing and retrieval with the OVIDIUS platform

Proceedings of the international conference on Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a novel temporal data segmentation approach based on a graph partition model. To find the optimal segmentation, which maintains maximal connectivity within the same segment while keeping minimum association between different ones, we adopt the min-max cut as an objective function. For temporal data, a linear time algorithm is designed by importing the temporal constraints. With multi-pair comparison strategy, the proposed method is more robust than the existing pair-wise comparison ones. The experiments on TRECVID benchmarking platform demonstrate the effectiveness of our approach.