题目（一）：Temporal Skeletonization on Sequential Data: Patterns, Categorization, and Visualization 

　　报告人：Prof. Hui Xiong (罗格斯，新泽西州立大学) 

　　时间：2014年8月13日10:00  

　　地点：中科院自动化所智能化大厦三层第二会议室  

　　报告摘要： 

　　Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in a sequence. With the growing complexity of real-world dynamic scenarios, it often requires more and more symbols to encode a meaningful sequence. This is so-called the “curse of cardinality” problem, which can impose significant challenges to the design of sequential analysis methods in terms of computational efficiency and practical use. Indeed, given the overwhelming scale and the heterogeneous nature of the sequential data, what is needed is a new vision and strategy to face the challenges. To this end, in this talk, we introduce a temporal skeletonization approach to proactively reduce the representation of sequences, so as to expose their hidden multi-level temporal structures. The key idea is to summarize the temporal correlations in an undirected graph. Then, the “skeleton" of the graph serves as a higher granularity on which hidden temporal patterns are more likely to be identified. As a matter of fact, the embedding topology of the graph can allow to translate the rich temporal content into metric space. This opens up new possibilities to explore, quantify, and visualize sequential data in the metric space. Finally, experimental results on real-world data have shown that the proposed approach can greatly alleviate the problem of curse of cardinality for the challenging tasks of sequential pattern mining and clustering. Also, the evaluation on a Business-to-Business (B2B) marketing application demonstrates that our approach can effectively discover critical buying paths from noisy marketing data. 

　　报告人简介： 

　　Dr. Hui Xiong is currently a Professor and the Vice Chair of the Management Science and Information Systems Department, and the Director of Rutgers Center for Information Assurance at Rutgers, the State University of New Jersey, where he received a two-year early promotion/tenure (2009), the Rutgers University  Board of Trustees Research Fellowship for Scholarly Excellence (2009), and the ICDM-2011 Best Research Paper Award (2011). Dr. Xiong received his Ph.D. in Computer Science from the University of Minnesota (UMN), USA, in 2005, the B.E. degree in Automation from the University of Science and Technology of China (USTC), China, and the M.S. degree in Computer Science from the National University of Singapore (NUS), Singapore. His general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive applications. He has published prolifically in refereed journals and conference proceedings (3 books, 60+ journal papers, and 60+ conference papers). He is the co-Editor-in-Chief of Encyclopedia of GIS by Springer, and an Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE) as well as the Knowledge and Information Systems (KAIS) journal. He has served regularly on the organization and program committees of numerous conferences, including as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), a Program Co-Chair for the IEEE 2013 International Conference on Data Mining (ICDM-2013), and a General Chair for the IEEE 2015 International Conference on Data Mining(ICDM-2015.) 

　　题目（二）：Large-scale Visual Search 

　　报告人：Prof. Tian Qi (德州大学圣安东尼奥分校) 

　　时间：2014年8月13日14:00   

　　地点：中科院自动化所智能化大厦三层第二会议室 

　　报告摘要： 

　　With the emergence of massive social multimedia data and prevalence of mobile visual search applications, techniques towards large-scale visual search and recognition are desired. Recent decade has witnessed the fast advance of large-scale image search, thanks to introduction of bag-of-visual-words model based on local invariant features and the scalable index structure. Generally, an image search system is involved with several key modules, including feature extraction, visual codebook learning, feature quantization, index strategy, scoring scheme, and post processing. Besides, post-processing techniques, such as geometric verification, query expansion and multi-modal fusion, can be plugged in to boost the retrieval performance. 

　　In the first part of the talk, I will make an overview of large-scale visual search and discuss two milestones. In the second part, I will figure out four key problems on visual feature representation, feature quantization, image indexing, and post-processing in the framework of image search. To address those problems, we have conducted comprehensive work. I will introduce our recent representative works and show the related demos. On feature representation, we have developed a set of binary features, including USB, COGE, etc, and designed a new regional feature. On feature quantization, I will introduce our work on codebook learning and efficient quantization. On image indexing, a series of our recently proposed indexing schemes will be introduced, such as cross-index, coupled multi-index, super-image index, cascade category-aware index. Last but not least, on post-processing, I will introduce our work on geometric verification and query expansion. In the third part, I will make a summarization and discuss the potential applications.  

　　报告人简介:　　

    Qi Tian is currently a Full Professor in the Department of Computer Science, the University of Texas at San Antonio (UTSA). During 2008-2009, he took one-year Faculty Leave at Microsoft Research Asia (MSRA) in the Media Computing Group. He received his Ph.D. in ECE from University of Illinois at Urbana-Champaign (UIUC) in 2002 and his B.E and M.S degrees from Tsinghua University and Drexel University in 1992 and 1996, respectively, all from electronic engineering. Dr. Tian’s research interests focus on multimedia information retrieval and computer vision and published over 240 refereed journal and conference papers. He received the Best Paper Award in PCM 2013, ACM ICIMCS 2012 and MMM 2013, a Top 10% Paper Award in MMSP 2011, the Best Student Paper Award in ICASSP 2006, and was a co-author of a Best Paper Candidate in PCM 2007. His research projects are funded by NSF, ARO, DHS, Google, FXPAL, NEC, SALSI, CIAS, Akiira Media Systems, HP and UTSA. He received 2010 ACM Service Award. He is the Guest Editors of IEEE Transactions on Multimedia, Journal of Computer Vision and Image Understanding, etc, and is the Associate Editor of IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) and in the Editorial Board of Journal of Multimedia (JMM) and Journal of Machine Vision and Applications (MVA).  He is the Guest or Adjunct Professor in Institute of Computing Technology, Chinese Academy of Science, Xi’an Jiaotong University, USTC, Zhejing University, Xidian University and a Chaired Professor in Tsinghua University.