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Dr. Tao MEI
Research Staff Member Media Computing Group | Microsoft Research Asia 5F Sigma, 49 Zhichun Road Beijing 100190, P. R. China
Tel: 86-10-5896 3036 Fax: 86-10-8809 7306 Email: |
Bio:
Dr. Tao Mei received the B.E. degree in automation and the Ph.D. degree in pattern recognition and intelligent systems from the University of Science and Technology of China, Hefei, in 2001 and 2006, respectively. He joined Microsoft Research Asia, Beijing, China, as a Researcher Staff Member, in 2006. He is a visiting professor of the Xidian University during 2009-2012. His current research interests include multimedia content analysis, computer vision, and internet multimedia applications such as search, advertising, management, social network, mobile applications. He is the author of five book chapters and over 80 journal and conference papers in these areas, and holds more than 20 filed international and U.S. patents or pending applications.
Dr. Mei serves as an Editorial Board Member for Journal of Multimedia, a Guest Editor for IEEE Multimedia for the Special Issue on "Knowledge Discovery over Community-Contributed Multimedia Data: Opportunities and Challenges," ACM Multimedia Systems for the Special Issue on "Multimedia Intelligent Services and Technologies," and Journal of Visual Communication and Image Representation for the Special Issue on "Large-Scale Image and Video Search: Challenges, Technologies, and Trends," and a Technical Reviewer for over ten prestigious international journals. He was the principle designer of the automatic video search system that achieved the best performance in the worldwide TRECVID evaluation in 2007. He received the Best Paper and Best Demonstration Awards in the ACM International Conference on Multimedia 2007, the Best Poster Paper Award in the IEEE International Workshop on Multimedia Signal Processing 2008, and the Best Paper Award in the ACM International Conference on Multimedia 2009.
He is a Member of the IEEE (2007-) and the ACM (2006-).
Representative Publications [Full list] [DBLP]
- Contextual In-Image Advertising
Tao Mei, Xian-Sheng Hua, Shipeng Li
ACM Multimedia, pp. 439-448, 2008.
ImageSense is an innovative contextual advertising system driven by images, which automatically associates relevant ads with an image rather than the entire text in a Web page and seamlessly inserts the ads in the nonintrusive areas within each individual image. ImageSense represents the first attempt towards contextual in-image advertising.
- VideoSense - Towards Effective Online Video Advertising
Tao Mei, Xian-Sheng Hua, Linjun Yang, Shipeng Li
ACM Multimedia, pp. 1075-1084, 2007.
VideoSense is a novel advertising system for online video service, which automatically associates the most relevant video ads with videos and seamlessly inserts the ads at the most appropriate positions within each video. Unlike most current video sites that only display a video ad at the beginning or the end of a video, VideoSense aims to embed more contextually relevant ads at less intrusive positions within video stream.
- Video Collage: Presenting a Video Sequence Using a Single Image
Tao Mei, Bo Yang, Shi-Qiang Yang, Xian-Sheng Hua
The Visual Computer, 25(1): 39-51, 2009.
Best Demo Award from SIGMM 2007
Video Collage is a kind of synthesized image that enable users to quickly browse the video content. Given a video, Video Collage is able to select the most representative images from the video, extract salient regions of interest (ROI) from these images, and seamlessly arrange ROI on a given canvas.
- When Multimedia Advertising Meets the New Internet Era
Xian-Sheng Hua, Tao Mei, Shipeng Li
IEEE Workshop on Multimedia Signal Processing, pp. 1-5, 2008.
Best Poster Award from MMSP 2008
The community-contributed media contents have become the primary sources for online advertising. Conventional ad-networks treat image and video advertising as general text advertising, while in MediaSense, we summarize the trends of online advertising and propose an innovative advertising model driven by the compelling contents of images and videos. We envision that the next trend of multimedia advertising would be game-like advertising.
- VideoReach: An Online Video Recommendation System
Tao Mei, Bo Yang, Xian-Sheng Hua, Linjun Yang, Shi-Qiang Yang, Shipeng Li
ACM SIGIR, pp. 767-768, 2007.
VideoReach is a novel online video recommendation system, which alleviates users' efforts on finding the most relevant videos according to current viewings without a sufficient collection of user profiles as required in traditional recommenders. In this system, video recommendation is formulated as finding a list of relevant videos in terms of multimodal relevance (i.e. textual, visual, and aural relevance) and user click-through.
- Coherent Image Annotation by Learning Semantic Distance
Tao Mei, Yong Wang, Xian-Sheng Hua, Shaogang Gong, Shipeng Li
IEEE CVPR, 2008.
We propose a novel approach to image annotation which learns a Semantic Distance by capturing the prior annotation knowledge and propagates the annotation of an image as a whole entity. A semantic distance function (SDF) is learned for each semantic cluster to measure the semantic similarity based on comparison relations of prior annotations. The training images in each cluster are ranked according to their SDF values and their annotations are then propagated as a whole entity to a new image.
- Home Video Visual Quality Assessment with Spatiotemporal Factors
Tao Mei, Xian-Sheng Hua, Cai-Zhi Zhu, He-Qin Zhou, Shipeng Li
IEEE Trans. on Circuits and Systems for Video Technology, 17(6): 699-706, 2007.
We present a novel spatio-temporal quality assessment scheme in terms of low-level content features for home videos. A type of temporal segment of video, sub-shot, is selected as the basic unit for quality assessment. A set of spatio-temporal visual artifacts are mined from each sub-shot based on particular characteristics of home videos. The relationship between the overall quality metric and these factors are exploited by user study, rule, and learning-based.
- Structure and Event Mining in Sports Video with Efficient Mosaic
Tao Mei, Xian-Sheng Hua
Multimedia Tools and Applications, 40(1): 89-110, 2008.
We propose a mosaic based approach to key-event as well as structure mining for sports video analysis. Mosaic is generated for each shot by a novel efficient mosaicing scheme, which constructs a global motion path and selects a best subset of frames for mosaicing. Based on mosaic, the structure and event in sports video are mined by the methods with prior knowledge and without prior knowledge.
- Last updated: Oct. 2009 -
