This paper studies evolutionary clustering, which is a recently hot topic with many important applications, noticeably in social network analysis. In this paper, based on the rece...
Tianbing Xu, Zhongfei (Mark) Zhang, Philip S. Yu, ...
The work presents the first effort to automatically annotate the semantic meanings of temporal video patterns obtained through unsupervised discovery processes. This problem is in...
Lexing Xie, Lyndon S. Kennedy, Shih-Fu Chang, Ajay...
We present algorithms for automatic feature selection for unsupervised structure discovery from video sequences. Feature selection in this scenario is hard because of the absence ...
In this paper, we tackle the problem of understanding the temporal structure of complex events in highly varying videos obtained from the Internet. Towards this goal, we utilize a...
The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...