Video temporal segmentation using support vector machine

Authors:
Shaohua Teng;Wenwei Tan
Affiliations:
Guangdong University of Technology, Guangzhou, P.R. China;Guangdong University of Technology, Guangzhou, P.R. China
Venue:
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Year:
2008

Citing 6
Cited 0

Segmentation of video by clustering and graph analysis

Computer Vision and Image Understanding
Techniques used and open challenges to the analysis, indexing and retrieval of digital video

Information Systems
Shot boundary detection algorithm in compressed domain based on adaboost and fuzzy theory

ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Video Segmentation via Temporal Pattern Classification

IEEE Transactions on Multimedia
Video partitioning by temporal slice coherency

IEEE Transactions on Circuits and Systems for Video Technology
A Formal Study of Shot Boundary Detection

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.01

Visualization

Abstract

A first step required to allow video indexing and retrieval of visual data is to perform a temporal segmentation, that is, to find the location of camera-shot transitions, which can be either abrupt or gradual. We adopt SVM technique to decide whether a shot transition exists or not within a given video sequence. Active learning strategy is used to accelerate training of SVM-classifiers. We also introduce a new feature description of video frame based on Local Binary Pattern (LBP). Cosine Distance is used to qualify the difference between frames in our works. The proposed method is evaluated on the TRECVID-2005 benchmarking platform and the experimental results reveal the effectiveness of the method.