Robust Object Re-Identification in Large Repository for Mobile Visual Search
发布时间:2018-03-25 浏览:498

报告题目:Robust Object Re-Identification in Large Repository for Mobile Visual Search

报告人:Zhu Li教授





报告摘要:Mobile visual search has many important applications in surveillance, security, virtual and augmented reality and e-commerce. A central technical problem to enable these application is the visual object identification against a very large repository. Robust local features that are invariant to the image formation process, good aggregation and compression schemes for the local features that offers indexing efficiency and matching accuracy, are the focus of the recent MPEG standardization effort on Compact Descriptor for Visual Search (CDVS).  In this talk, I will review the key technical challenges to the CDVS pipeline, and covering the novel contributions made in the CDVS work on alternative interesting points detection, more efficient interesting points aggregation scheme, indexing / hashing issues for object re-identification against large repository, and retrieval system optimization, as well as the future directions of the research in this area with new depth sensors and video inputs.


报告人简介:Zhu Li is an associated professor with the Dept of CSEE, University of Missouri, Kansas City(UMKC), USA, directs the NSF I/UCRC Center for Big Learning at UMKC. He received his PhD from Electrical & Computer Engineering from Northwestern University in 2004, and was AFRL Faculty Fellow at Cyber Warfare Center and UAV Research Center at the US Air Force Academy, Summer, 2016 and 2017. He was Sr. Staff Researcher/Sr. Manager with Samsung Research America's Multimedia Core Standards Research Lab in Dallas, from 2012-2015, Sr.Staff Researcher with FutureWei(Huawei)'s Media Lab in Bridgewater, NJ, from 2010-2012,  Assistant Professor with the Dept of Computing, The HongKong Polytechnic University from 2008 to 2010, and a Principal Staff Research Engineer with the Multimedia Research Lab (MRL), Motorola Labs, Schaumburg, Illinois, from 2000 to 2008. His research interests include image/video analysis, compression, and communication and associated optimization and machine learning tools. He has 46 issued or pending patents, 100+ publications in book chapters, journals, conference proceedings and standards contributions in these areas. He is an IEEE senior member, elected member of the IEEE Multimedia Signal Processing (MMSP) Technical Committee (2014-2020), elected Steering Chair (2016-18) of the IEEE Multimedia Communication Technical Committee  (MMTC), elected member of the IEEE Circuits & System Society Multimedia Systems & Application (MSA) Tech Committee. He is an Associated Editor for IEEE Trans. On Multimedia (2015~),  IEEE Trans. on Circuits & System for Video Technology (2016~), Springer Journal on Signal Processing Systems (2015~), co-editor for the Springer-Verlag book on Intelligent Video Communication: Techniques and Applications. He is general co-chair for IEEE VCIP 2017, Special Session co-Chair for IEEE ICME 2017, he served on numerous conference and workshop TPCs and was area chair for IEEE ICIP 2015, 2016, 2017, ICME 2015, 2016, and symposium co-chair at IEEE ICC'2010, and IEEE Globecom 2017. He served on the Best Paper Award Committee for IEEE ICME 2010. He received a Best Paper Award from IEEE ICME at Toronto, 2006, and a Best Paper Award from IEEE ICIP at San Antonio, 2007.