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CVPR 2010 was held in San Francisco, 13-18 June 2010. This special issue contains extended versions of the three award winning papers:

    “Visual Event Recognition in Videos by Learning from Web Data,” LixinDuan, Dong Xu, Ivor Wai-Hung Tsang, and JieboLuo (Best Student Paper).

    “Efficient Computation of Robust Low-Rank Matrix Approximations using the $L_1$ Norm,” Anders Eriksson and Anton van den Hengel (Best Paper).

    “Recognizing Human-Object Interactions in Still Images by Modeling the Mutual Context of Objects and Human Poses,” Bangpeng Yao and Li Fei-Fei (Best Paper Honorable Mention).

The issue also contains extended versions of six of the eight other papers recommended for consideration for an award. All papers have been through the normal reviewing process for TPAMI.
We received 1,724 submissions to the conference, a substantial increase from previous years. To select papers from these submissions, we invited 45 well-known vision researchers to act as Areas Chairs (ACs) and recruited an expert team of 718 reviewers from the broader computer vision community. The ACs accepted 78 papers as orals (4.5 percent) and 383 papers as posters (22.2 percent), with an overall acceptance rate of 26.7 percent. Area Chairs recommended 11 papers for consideration for an award.
The Awards Committee consisted of four senior members of the vision community, namely, Jan Olof Eklundh, Martial Hebert, Michal Irani, and Shree Nayar. The committee began by reading all nominated papers and providing an initial ranking of them, along with their analysis of the contribution. A spirited exchange of e-mails followed, in which the committee debated the contributions of each paper. While it is always difficult to single out just a few papers from the many excellent pieces of work that appear in CVPR, the committee did reach a consensus on three papers they all felt were very deserving of awards.
The Longuet-Higgins Prize for fundamental contributions in computer vision that have withstood the test of time was awarded to the following two papers that were published in the Proceedings of CVPR 2000:

    “Efficient Matching of Pictorial Structures,” Pedro F. Felzenszwalb and Daniel P. Huttenlocher.

    “Real-Time Tracking of Non-Rigid Objects Using Mean Shift,” Dorin Comaniciu, Visvanathan Ramesh, and Peter Meer.

We wish to thank the members of the Awards Committee, the Area Chairs, and reviewers, as well as the TPAMI reviewers of the award winning and nominated papers submitted to this special issue.
Trevor Darrell
David Hogg
David Jacobs
Guest Editors and CVPR 2010 Program Cochairs

    T. Darrell is with the Computer Science Division, University of California, Berkeley, and the International Computer Science Institute (ICSI), 1947 Center Street, Suite 600, Berkeley, CA 94704.


    D. Hogg is with the School of Computing, University of Leeds, Leeds, UK. E-mail:

    D. Jacobs is with the Department of Computer Science, AV Williams Building, University of Maryland, College Park, College Park, MD 20742.


For information on obtaining reprints of this article, please send e-mail to:

Trevor Darrell received the BSE degree from the University of Pennsylvania in 1988, having started his career in computer vision as an undergraduate researcher in Ruzena Bajcsy's GRASP lab. received the SM and PhD degrees from MIT in 1992 and 1996, respectively. He is on the faculty of the Computer Science Division of the Electrical Engineering and Computer Science Department at the University of Ccalifornia, Berkeley, and is the vision group lead at ICSI. His group develops algorithms to enable multimodal conversation with robots and mobile devices, and methods for object and activity recognition on such platforms. His interests include computer vision, machine learning, computer graphics, and perception-based human computer interfaces. He was previously on the faculty of the MIT Electrical Engineering and Computer Science Department from 1999-2008, where he directed the Vision Interface Group. He was a member of the research staff at Interval Research Corporation from 1996-1999. He is a member of the IEEE Computer Society.

David Hogg received the BSc degree in applied mathematics from the University of Warwick, the MSc degree in computer science from the University of Western Ontario, and the PhD degree from the University of Sussex. He was on the faculty of the School of Cognitive and Computing Sciences at the University of Sussex from 1984 until 1990, when he was appointed a full professor of artificial intelligence at the University of Leeds, where he is now Pro-Vice-Chancellor for Research and Innovation. During 1999-2000, he was a visiting professor at the MIT Media Lab in Cambridge. He has been an associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (2006-2010) and program cochair of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. His current research is on activity analysis, dealing especially with learning and the integration of qualitative and quantitative representations. He is a fellow of the ECCAI and a member of the IEEE Computer Society.

David W. Jacobs received the BA degree from Yale University in 1982. From 1982 to 1985, he worked for Control Data Corporation on the development of data base management systems, and attended graduate school in computer science at New York University. From 1985 to 1992, he attended MIT, where he received the MS and PhD degrees in computer science. He is a professor in the Department of Computer Science at the University of Maryland with a joint appointment in the University's Institute for Advanced Computer Studies (UMIACS). From 1992 to 2002, he was a research scientist and then a senior research scientist at the NEC Research Institute. In 1998, he spent a sabbatical at the Royal Institute of Technology (KTH) in Stockholm, and in 2008 spent a sabbatical at the Ecole Normale Supérieure de Cachan. In 2002, he joined the Computer Science Department at the University of Maryland. His research has focused on human and computer vision, especially in the areas of object recognition and perceptual organization. He has also published articles in the areas of motion understanding, memory and learning, computer graphics, human computer interaction, and computational geometry. He has served as an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, and has assisted in the organization of many workshops and conferences, including serving as program cochair for CVPR 2010. He and his coauthors received honorable mention for the best paper award at CVPR 2000. He also coauthored a paper that received the best student paper award at UIST 2003. In collaboration with researchers at Columbia University and the Smithsonian Institution he created Leafsnap, an app that uses computer vision for plant species identification, for which he and his collaborators have been awarded the 2011 Edward O. Wilson Biodiversity Technology Pioneer Award.
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