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Tao
MEI, Ph.D.
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Research Staff Member
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5F Sigma, 49 Zhichun Road
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Beijing 100190, P. R. China
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| Tel: | + 86 - 10 - 5896 3036 | ||
| Fax: | + 86 - 10 - 8809 7306 | ||
| Email: | |||
| Personal: | |||
| CV: | [PDF] | ||
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Bio Dr. Tao Mei is a research staff member at Microsoft Research Asia. He received the B.E. degree in automation and Ph.D. degree in pattern recognition and intelligent system from the University of Science and Technology of China, Hefei, China, in 2001 and 2006, respectively. His current research interests include multimedia content analysis, computer vision, pattern recognition, and online multimedia applications such as multimedia search, advertising, recommendation, presentation, and social media. He has authored 5 book chapters and over 70 journal and conference papers in these areas, and holds about 20 filed international and US patents or pending applications. He serves as an Editorial Board Member of Journal of Multimedia and Advances in Multimedia, a Guest Editor of IEEE Multimedia for the Special Issue on "Knowledge Discovery over Community-Contributed Multimedia Data: Opportunities and Challenges," Journal of Visual Communication and Image Representation Special Issue on "Large-Scale Image and Video Search: Challenges, Technologies, and Trends," and ACM Multimedia Systems Journal for the Special Issue on "Multimedia Intelligent Services and Technologies," a Technical Program Committee Member for numerous international conferences, and a Reviewer for over 10 prestigious international journals. Dr. Mei is the principle designer of the automatic video search system that achieves the best performance in the worldwide TRECVID evaluation in 2007. He received the Best Paper and the Best Demonstration Awards in ACM International Conference on Multimedia 2007, and the Best Poster Award in IEEE International Workshop on Multimedia Signal Processing 2008. He is now a visiting professor of the Xidian University. He is a member of the
Institute of Electrical and Electronics Engineers (IEEE) and the
Association for Computing Machinery (ACM). He is listed in Marquis
Who's Who of Emerging Leaders, 2007, and Marquis Who's Who in the World,
2009. |
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| * New: Online Multimedia Advertising, a book to be published by IGI Global. [Call for book chapter proposals] | |||
| *Notes: I am always looking for outstanding research and development interns. If you are interested in multimedia related research and projects, please do not hesitate to send your resume to me. | |||
| Representative Publications [full list by year] [full list by categories] [DBLP] | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Multi-Graph-Based
Query-Independent Learning for Video Search Yuan Liu, Tao Mei, Xiuqing Wu, Xian-Sheng Hua IEEE Trans. on Circuits and Systems for Video Technology, 2009. |
We propose a novel query-independent learning approach based on multi-graph to video search, which learns the relevance information existing in the query-shot pairs. The proposed approach, named MG-QIL, is more general and suitable for a real-world video search system as the learned relevance is independent on any query and any dataset. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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CrowdReranking: Exploring
Multiple Search Engines for Visual Search Reranking Yuan Liu, Tao Mei, Xian-Sheng Hua ACM SIGIR, 2009. |
CrowdReranking is a new method for visual search reranking, which is characterized by mining relevant visual patterns from image search results of multiple search engines which are available on the Internet. Observing that diffrent search engines might have diffrent data sources for indexing and methods for ranking, it is reasonable to assume that there exist diffrent search results yet certain common visual patterns relevant to a query among those results. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 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 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Multi-Layer
Multi-Instance Learning for Video Concept Detection Zhiwei Gu, Tao Mei, Xian-Sheng Hua, et al. IEEE Trans. on Multimedia, 10(8): 1605-1616, 2008. |
Video is essentially a kind of media with multi-layer (ML) structure. We call such multi-layer structure and the "bag-instance" relations embedded in the structure as Multi-Layer Multi-Instance (MLMI) setting. We systematically study both ML structure and MI relations embedded in video content and formulate video concept detection as a MLMI learning problem. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Structure
and Event Mining in Sports Video with Efficient Mosaic |
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Professional Activities Associate Editor | Editorial Board Member
Special Session Organizer
Organization Committee Member
Technical Program Committee Member
Review for:
Awards
The interns I mentored at MSR Asia
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