Video Shot Detection Using Hidden Markov Models with Complementary Features

Authors:
Weigang Zhang;Jianqiu Lin;Xiaopeng Chen;Qingming Huang;Yang Liu
Affiliations:
Harbin Institute of Technology at Weihai, China;Harbin Institute of Technology at Weihai, China;Harbin Institute of Technology at Weihai, China;Graduate School of Chinese Academy of Sciences, China;Harbin Institute of Technology, China
Venue:
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Year:
2006

Citing 0
Cited 4

Video shot boundary detection algorithm using LZW compression technique

SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
Concurrent transition and shot detection in football videos using fuzzy logic

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Optimal shot detection and recognition using Shiryaev-Roberts statistics

Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Video cut detection using dominant color features

Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

Shot detection is the first stage of video analysis. In this paper, we present a machine learning based shot detection approach using Hidden Markov Models (HMMs), in which both the color and shape clues are utilized. Its advantages are twofold. First, the temporal characteristics of different shot transitions are exploited and an HMM is constructed for each type of shot transitions, including cut and gradual transitions. As trained HMMs are used to recognize the shot transition patterns automatically, it does not suffer from any trouble of threshold selection problem. Second, two complementary features, statistical corner change ratio (SCCR) and HSV color histogram difference, are used. The former summarizes the shape well whereas the latter summarizes the appearance well. Experimental results on a set of test videos demonstrate the efficacy of this shot detection approach.