Publication list [by year] [by topic]

	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. [pdf] [Matlab code]
	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. [pdf]
	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. [pdf]
Active Visual Category Learning	Cost-Sensitive Active Visual Category Learning. S. Vijayanarasimhan and K. Grauman. Abstract presented at the Learning Workshop, Clearwater FL, April 2009. [abstract] [slides] 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. [pdf] 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, Dec. 2008. [pre-print] [slides]
Visual Search: Hashing with Learned Metrics	Efficiently Searching for Similar Images. K. Grauman. Invited article to appear in the Communications of the ACM, 2009. [pre-print] 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, Dec. 2008. [pre-print] [extended version] 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] [pdf] [slides (ppt)] Fast Similarity Search for Learned Metrics. B. Kulis, P. Jain, and K. Grauman. To appear, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), December 2009. [selected as the Spotlight Paper for the December issue]
	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. [pdf] [poster (pdf)] [poster (ppt)] [Semantic Robot Vision Challenge slides (ppt)]
	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. [pdf] [slides (ppt)] 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. [link]
	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. [pdf]
	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. [pdf] Gaussian Processes for Object Categorization. A. Kapoor, K. Grauman, R. Uratsun, and T. Darrell. To appear, International Journal of Computer Vision (IJCV), 2009. [link]
Pyramid Match Kernel	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.   [pdf]  [ppt slides] [code] [Updated results on the Caltech101 data set] 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. [pdf] 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. [pdf] [project page] [code] K. Grauman. Matching Sets of Features for Efficient Retrieval and Recognition, Ph.D. Thesis, MIT, 2006. [pdf] (35.8 MB)
	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. [pdf]
	Approximate Correspondences in High Dimensions. K. Grauman and T. Darrell. In Advances in Neural Information Processing Systems 19 (NIPS) 2007. [ps] [pdf] [code] [poster (pdf)] [poster (ppt)]
	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. [pdf] [ppt slides]
	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. [pdf] 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. [pdf]
	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.  [pdf]
	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.  [pdf] K. Grauman.  A Statistical Image-Based Shape Model for Visual Hull Reconstruction and 3D Structure Inference.  Master's thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May 2003.  MIT AI Tech Report, AITR-2003-007. [ps] or [pdf] 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.   [pdf]
	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.  [pdf] 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. [pdf]   [SpringerLink]
	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. 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. [pdf]