题 目：Large-scale Visual Search

主讲人：田奇 教授

主 持：李进 教授

时 间：2017年5月18日（星期四）上午10:15-11:15

地 点：行政西楼前座428会议室

主讲人简介：

 Qi Tian is currently a Full Professor in the Department of Computer Science, the University of Texas at San Antonio (UTSA)，Fellow of IEEE.

Dr. Tian received his Ph.D. in ECE from University of Illinois at Urbana-Champaign (UIUC) in 2002. Dr. Tian’s research interests include multimedia information retrieval, computer vision, pattern recognition and bioinformatics and published over 370 refereed journal and conference papers (including 88 IEEE/ACM Transactions papers and 67 CCF Category A conference papers).

Dr. Tian research projects are funded by ARO, NSF, DHS, Google, FXPAL, NEC, SALSI, CIAS, Akiira Media Systems, HP, Blippar and UTSA. He received 2017 UTSA President’s Distinguished Award for Research Achievement, 2016 UTSA Innovation Award, 2014 Research Achievement Awards from College of Science, UTSA, 2010 Google Faculty Award, and 2010 ACM Service Award. He is the associate editor of IEEE Transactions on Multimedia (TMM), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), ACM Transactions on Multimedia Computing, Communications. Dr. Tian is the Guest Editor of IEEE Transactions on Multimedia, Journal of Computer Vision and Image Understanding, etc.

讲座内容简介：

Coupled with the massive social multimedia data and mobile visual search applications, techniques towards large-scale visual search and recognition are emerging. Current state-of-the-art image search algorithms and systems are motivated by the classic bag-of-visual-words model and the scalable index structure, and further powered by the deep learning techniques. In the first part of the talk, I will introduce those related works in each module as mentioned above and discuss the key research problems. In the second part, I will introduce our research work on large scale image search. We have done comprehensive work on feature representation, feature quantization, scalable indexing, spatial verification, et al. Several representative works will be discussed and the related demos will be shown. In the third part, I will discuss the potential research directions and promising applications on large scale image search.