Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rushes video summarization by object and event understanding
Proceedings of the international workshop on TRECVID video summarization
A distance measure for repeated takes of one scene
The Visual Computer: International Journal of Computer Graphics
Rushes video summarization and evaluation
Multimedia Tools and Applications
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Retake detection is useful for many applications of video summarization. It is a challenging task since different takes of the same scene are usually of different lengths; or have been recorded under different environment conditions. A general approach to solve this problem is to decompose the input video sequence into sub-sequences and then group these sub-sequences into clusters. By combining with temporal information, the clustering result is used to find take and scene boundaries. One of the most difficult steps in this approach is to cluster sub-sequences. Most of previous approaches only use one keyframe for representing one sub-sequence and extract features such as color and texture from this keyframe for clustering. We propose another approach to improve the performance by combining the motion feature extracted from each sub-sequence and the features extracted from each representing keyframe. Experiments evaluated on the standard benchmark dataset of TRECVID BBC Rushes 2007 show the effectiveness of the proposed method.