Publication list: by year [by topic] [with images/code/pdfs]

 

2009

 

Fast Similarity Search for Learned Metrics.   B. Kulis, P. Jain, and K. Grauman.   In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), December, 2009.

 

Kernelized Locality-Sensitive Hashing for Scalable Image Search.  B. Kulis and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Kyoto, Japan, October, 2009.

 

Foreground Focus: Unsupervised Learning from Partially Matching Images.  Y. J. Lee and K. Grauman.  In International Journal of Computer Vision (IJCV), Vol. 85, No. 2, 2009.

 

Gaussian Processes for Object Categorization.  A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell.  To appear, International Journal of Computer Vision (IJCV), 2009.

 

Efficiently Searching for Similar Images.  K. Grauman.  Invited article to appear in the Communications of the ACM, 2009. 

 

What’s It Going to Cost You?: Predicting Effort vs. Informativeness for Multi-Label Image Annotations.  S. Vijayanarasimhan and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, June 2009.

 

Shape Discovery from Unlabeled Image Collections.  Y. J. Lee and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, June 2009.

 

Observe Locally, Infer Globally: a Space-Time MRF for Detecting Abnormal Activities with Incremental Updates.  J. Kim and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, June 2009. 

 

 

2008

 

Multi-Level Active Prediction of Useful Image Annotations for Recognition.  S. Vijayanarasimhan and K. Grauman.  In Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2008.

 

Online Metric Learning and Fast Similarity Search.  P. Jain, B. Kulis, I. Dhillon, and K. Grauman.  In Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2008.

 

Fast Image Search for Learned Metrics.  P. Jain, B. Kulis, and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, June 2008.  [Best Student Paper Award]   

 

Keywords to Visual Categories: Multiple-Instance Learning for Weakly Supervised Object Categorization.  S. Vijayanarasimhan and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, June 2008.

 

Foreground Focus: Finding Meaningful Features in Unlabeled Images. Y. J. Lee and K. Grauman.  In Proceedings of the British Machine Vision Conference (BMVC), Leeds, U.K., September 2008.

 

Watch, Listen & Learn: Co-training on Captioned Images and Videos.  S. Gupta, J. Kim, K. Grauman, and R. Mooney.  In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Antwerp, Belgium, September 2008. 

 

 

2007

 

Active Learning with Gaussian Processes for Object Categorization.  A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell.  In Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, October 2007.

 

Pyramid Match Hashing: Sub-Linear Time Indexing Over Partial Correspondences.  K. Grauman and T. Darrell.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, MN, June 2007.

 

Approximate Correspondences in High Dimensions.  K. Grauman and T. Darrell.   In Advances in Neural Information Processing Systems 19 (NIPS) 2007.    

 

The Pyramid Match: Efficient Learning with Partial Correspondences.  K. Grauman.   In Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), Nectar track, Vancouver, Canada, July 2007.  

 

The Pyramid Match Kernel: Efficient Learning with Sets of Features.  K. Grauman and T. Darrell.  Journal of Machine Learning Research (JMLR), 8 (Apr): 725--760, 2007. 

 

 

2006

 

Matching Sets of Features for Efficient Retrieval and Recognition.  K. Grauman.  Ph.D. Thesis, MIT, 2006. 

 

Unsupervised Learning of Categories from Sets of Partially Matching Image Features.  K. Grauman and T. Darrell.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New York City, NY, June 2006. 

 

 

2005

The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features.  K. Grauman and T. Darrell.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Beijing, China, October 2005.  

Avoiding the ``Streetlight Effect'': Tracking by Exploring Likelihood Modes.  D. Demirdjian, L. Taycher, G. Shakhnarovich, K. Grauman, and T. Darrell.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Beijing, China, October 2005.  

Efficient Image Matching with Distributions of Local Invariant Features.  K. Grauman and T. Darrell.  In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, June 2005.  

A Picture is Worth a Thousand Keywords: Image-Based Object Search on a Mobile Platform.  T. Yeh, K. Grauman, K. Tollmar, and T. Darrell.  In CHI 2005, Conference on Human Factors in Computing Systems, Portland, OR, April 2005. 

 

2004

 

Fast Contour Matching Using Approximate Earth Mover's Distance.  K. Grauman and T. Darrell.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington DC, June 2004.

Virtual Visual Hulls: Example-Based 3D Shape Inference from a Single Silhouette.  K. Grauman, G. Shakhnarovich, and T. Darrell.  In Proceedings of the 2nd Workshop on Statistical Methods in Video Processing, in conjunction with ECCV, Prague, Czech Republic, May 2004.

 

2003

Inferring 3D Structure with a Statistical Image-Based Shape Model.  K. Grauman, G. Shakhnarovich, and T. Darrell.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Nice, France, October 2003. 

A Statistical Image-Based Shape Model for Visual Hull Reconstruction and 3D Structure Inference.  K. Grauman.  Master's thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May 2003.  MIT AI Tech Report, AITR-2003-007.  

A Bayesian Approach to Image-Based Visual Hull Reconstruction.  K. Grauman, G. Shakhnarovich, and T. Darrell.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Madison, WI, June 2003. 

Communication via Eye Blinks and Eyebrow Raises: Video-Based Human-Computer Interfaces. K. Grauman, M. Betke, J. Lombardi, J. Gips, and G. Bradski.  Universal Access in the Information Society, 2(4) pp. 359-373, Springer-Verlag Heidelberg, November 2003.

 

2001

Communication via Eye Blinks: Detection and Duration Analysis in Real Time.  K. Grauman, M. Betke, J. Gips, and G. Bradski.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Lihue, HI, December 2001.