Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39989
Title: An unified framework for shot boundary detection via active learning
Authors: Chua, T.-S. 
Feng, H. 
Chandrashekhara, A.
Issue Date: 2003
Source: Chua, T.-S.,Feng, H.,Chandrashekhara, A. (2003). An unified framework for shot boundary detection via active learning. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2 : 845-848. ScholarBank@NUS Repository.
Abstract: Video shot boundary detection is an important step in many video processing applications. We observe that video shot boundary is a multi-resolution edge phenomenon in the feature space. In this paper, we expanded our previous temporal multi-resolution analysis (TMRA) work by introducing the new feature vector based on motion. Further we employ the support vector machine (SVM) to refine the classification of shot boundaries. The resulting framework has been tested on the MPEG 7 video data set, and has been shown to have good accuracy for both the detection of abrupt and gradual transitions as well as their boundaries. It also has good noise tolerance characteristics.
Source Title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/39989
ISSN: 15206149
Appears in Collections:Staff Publications

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