caa a22 4500 445871407 CHVBK 20180317145512.0 cr unu---uuuuu 170323e20110501xx s 000 0 eng 10.1007/s10618-010-0195-5 doi (NATIONALLICENCE)springer-10.1007/s10618-010-0195-5 Hierarchical visual event pattern mining and its applications [Elektronische Daten] [Peng Cui, Zhi-Qiang Liu, Li-Feng Sun, Shi-Qiang Yang] In this paper, we propose a hierarchical visual event pattern mining approach and utilize the patterns to address the key problems in video mining and understanding field. We classify events into primitive events (PEs) and compound events (CEs), where PEs are the units of CEs, and CEs serve as smooth priors and rules for PEs. We first propose a tensor-based video representation and Joint Matrix Factorization (JMF) for unsupervised primitive event categorization. Then we apply frequent pattern mining techniques to discover compound event pattern structures. After that, we utilize the two kinds of event patterns to address the applications of event recognition and anomaly detection. First we extend the Sequential Monte Carlo (SMC) method to recognition of live, sequential visual events. To accomplish this task we present a scheme that alternatively recognizes primitive and compound events in one framework. Then, we categorize the anomalies into abnormal events (never seen events) and abnormal contexts (rule breakers), and the two kinds of anomalies are detected simultaneously by embedding a deviation criterion into the SMC framework. Extensive experiments have been conducted which demonstrate that the proposed approach is effective as compared to other major approaches. The Author(s), 2010 Video mining nationallicence Matrix factorization nationallicence Event recognition nationallicence Anomaly detection nationallicence Cui Peng Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China aut Liu Zhi-Qiang School of Creative Media, City University, Kowloon, Hong Kong aut Sun Li-Feng Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China aut Yang Shi-Qiang Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/3(2011-05-01), 467-492 1384-5810 22:3<467 2011 22 10618 https://doi.org/10.1007/s10618-010-0195-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0195-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Cui Peng Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China aut NATIONALLICENCE 700 1- Liu Zhi-Qiang School of Creative Media, City University, Kowloon, Hong Kong aut NATIONALLICENCE 700 1- Sun Li-Feng Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China aut NATIONALLICENCE 700 1- Yang Shi-Qiang Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/3(2011-05-01), 467-492 1384-5810 22:3<467 2011 22 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer