Publication list [by year] [by topic]
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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] |
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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] |
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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] |
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Active Visual Category Learning |
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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] |
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Visual Search: Hashing with Learned
Metrics |
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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, 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), 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] |
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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), |
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Foreground
Focus: Finding Meaningful Features in Unlabeled Images. Y. J. Lee and K.
Grauman. In Proceedings of the
British Machine Vision Conference (BMVC), 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] |
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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), |
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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] |
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Pyramid Match Kernel |
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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), The
Pyramid Match: Efficient Learning with Partial Correspondences. K. Grauman. In Proceedings
of the Association for the Advancement of Artificial Intelligence (AAAI),
Nectar track, 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) |
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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),
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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)] |
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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),
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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), 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), |
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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, |
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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, K.
Grauman. A Statistical Image-Based Shape Model for Visual 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), |
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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), 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] |
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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 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),
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