Fast search and image retrieval
Unsupervised / semi-supervised visual discovery
Pyramid matching and local feature correspondences
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] [code]
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. [pdf]
Efficiently Searching for Similar Images. K. Grauman. Invited article to appear in the Communications of the ACM, 2009. [extended preprint]
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. [pdf]
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]
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]
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]
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]
Cost-Sensitive Active Visual Category Learning. S. Vijayanarasimhan and K. Grauman. Abstract in the Learning Workshop (The Snowbird Workshop), Clearwater, FL, April 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, December 2008. [pdf] [supplementary]
Gaussian Processes for Object Categorization. A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell. To appear, International Journal of Computer Vision (IJCV), 2009. [link]
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]
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]
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]
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]
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]
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]
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]
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]
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. [pdf]
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]
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]
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]
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]
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]
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]
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]
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. [link]
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]