AUTOMATIC VIDEO SUMMARIZATION BY DISCOVERING FREQUENT PATTERNS WITH SUPPORT VECTOR CLUSTERING
In this paper, we present a novel approach for automatic video summarization by discovering frequent shot patterns with support vector clustering. The frequent shot patterns in video are defined as a sequence of video shots that occur frequently within a time interval. First, support vector clustering is implemented to cluster similar video shots into groups based on the shot boundary detection. Then frequent shot patterns are extracted in order to remove the visual-content redundancy among video content clusters and generate the video summary. The experiments show that support vector clustering combined with frequent shot patterns discovery is an effective approach for video summarization.
Support Vector Clustering Frequent Patterns Discovery Video Summarization
RUO-GUI XIAO YUE-TING ZHUANG FEI WU
Institute of Artificial Intelligence of Zhejiang University Zhejiang University, Hangzhou P.R.China, 310027
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
1174-1177
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）