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Keynote 1: Semantic Image and Video Adaptation for Mobile Device Access
Abstract: Recent proliferation of mobile devices with video capture and display capabilities has resulted in a paradigm shift trends in video coding, processing, and adaptation. One major challenge in mobile media access is the adaptation of digital image and video for mobile devices with limited display sizes. Intelligent adaptation based on content semantics can provide much better adaptation than simple resolution reduction. This talk will present examples in semantic adaptation of image and video for mobile device access based on the understanding of media semantics. Some detailed analysis and simulation results will be shown to demonstrate that innovative approaches can be designed to meet the technical challenges in future networked mobile media entertainment and applications.
Biography: Chang Wen Chen is a Professor of Computer Science and Engineering at the University at Buffalo, State University of New York. He has been Allen Henry Endow Chair Professor of Electrical and Computer Engineering at the Florida Institute of Technology from July 2003 to December 2007. He was on the faculty of Electrical Engineering Department at the University of Rochester from 1992 to 1996, on the faculty of Electrical and Computer Engineering Department at the University of Missouri-Columbia from 1996 to 2003.
Currently, he is the Editor-in-Chief for IEEE Trans. Circuits and Systems for Video Technology. He has been an Associate Editor for several IEEE Transactions and Journals. He has served as Conference Chair for several major IEEE and SPIE conferences related to multimedia video communications and signal processing. His current research interests include high definition digital entertainment, reliable and secure multimedia communications over mobile wireless channels; digital video coding, processing, analysis, and embedded implementation, distributed source coding and digital signal processing for communications; and collaborative signal processing and data aggregation for sensor networks. His research is supported by NSF, DARPA, Air Force, NASA, Whitaker Foundation, Intel, Kodak and Huawei.
He received his BS from University of Science and Technology of China in 1983, MSEE from University of Southern California in 1986, and Ph.D. from University of Illinois at Urbana-Champaign in 1992. He was elected an IEEE Fellow for his contributions in digital image and video processing, analysis, and communications, and elected an SPIE Fellow for his contributions in electronic imaging and visual communications.
Keynote 2: Learning Concepts by Modeling Relationships
Abstract:Supporting multimedia search has emerged as an important research topic. There are three paradigms on the research spectrum that ranges from the least automatic to the most automatic. On the far left end, there is the pure manual labeling paradigm that labels multimedia content, e.g., images and video clips, manually with text labels and then use text search to search multimedia content indirectly. On the far right end, there is the content-based search paradigm that can be fully automatic by using low-level features from multimedia analysis. In recent years, a third paradigm emerged which is in the middle: the annotation paradigm. Once the concept models are trained, this paradigm can automatically detect/annotate concepts in unseen multimedia content. This talk looks into this annotation paradigm. Specifically, this talk argues that within the annotation paradigm, the relationship-based annotation approach outperforms other existing annotation approaches, because individual concepts are considered jointly instead of independently. We will use within concept relationship, between concept relationship and blind concept relationship to illustrate the idea..
Biography: Yong Rui is a Director of Microsoft China R&D Group from 2006 to present. From 1999 ¨C 2006, Dr. Rui was leading the Multimedia Collaboration team at Microsoft Research, Redmond, USA.
Dr. Rui contributes significantly to image and video analysis, indexing, and retrieval ¡ª from the fundamental framework,to algorithms, and to practical solutions. According to Google Scholar citation, two of Dr. Rui¡¯s papers have been each cited 1,000+ times and 12 other papers have each been cited 100+ times. His most distinctive contribution is in establishing a paradigm-shifting relevance feedback framework in image indexing and retrieval. Dr. Rui holds 40 issued and pending US and international patents in the above research fields.
Dr. Rui is an Associate Editor of ACM Trans. on Multimedia Computing, Communication and Applications (TOMCCAP), IEEE Trans. on Circuits and Systems for Video Technologies (CSVT), and is on the editorial board of IEEE Multimedia Magazine. He was an Associate Editor of IEEE Trans. on Multimedia (2004-2008), ACM/Springer Multimedia Systems Journal (2004-2006), and International Journal of Multimedia Tools and Applications (2004-2006). He also serves on the Advisory Board of IEEE Trans. on Automation Science and Engineering. Dr. Rui is a Senior Member of IEEE and a Distinguished Member of ACM.
Organized by
National Lab of Pattern Recognition (NLPR)
Institute of Automation Chinese Academy of Sciences (CASIA)
