UT-Austin Computer Vision Group Software

Please find software links below, together with the associated papers.

Boundary-preserving dense local region extraction (BPLRs)

BPLR feature detector, plus some descriptors for the extracted BPLRs, including HOG, chordiogram, and color histogram. Written in MATLAB and tested in Linux 32 and 64 bits.

Boundary-Preserving Dense Local Regions. J. Kim and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011. (Oral) [pdf]

Kernelized locality sensitive hashing (KLSH)

Kernelized hashing algorithm which allows sub-linear time search under an arbitrary kernel function. MATLAB code.

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]

Efficient region search object detection

Fast branch-and-cut approach to identify the subregion in an image that maximizes the classifier's score, for family of additive classifiers.

Efficient Region Search for Object Detection. S. Vijayanarasimhan and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011. [pdf]

Budgeted batch active learning

Batch-mode active learning algorithm to select the set of examples that appear most informative to an SVM, such that their total associated annotation cost meets a given budget.

Far-Sighted Active Learning on a Budget for Image and Video Recognition. S. Vijayanarasimhan, P. Jain, and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010. [pdf]

Object-graphs

The full pipeline for context-aware discovery with object-graphs. Implemented in Matlab

Object-Graphs for Context-Aware Category Discovery. Y. J. Lee and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010. (Oral) [pdf]

Multi-task feature learning

Learning with Whom to Share in Multi-task Feature Learning. Z. Kang, K. Grauman, and F. Sha. In Proceedings of the International Conference on Machine Learning (ICML), Bellevue, WA, July 2011. [pdf]

Multi-level multi-class VOI active selection

Implementation of Value-of-Information active learning criterion for multi-level multi-class case, as proposed in the CVPR 2009 paper

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]

MIL training of object categories and MIL Bag-reweighting code

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]
Used for the Semantic Robot Vision Challenge in 2008

Pyramid match kernel - John Lee's LIBPMK: a pyramid match toolkit

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

Incremental SVM code

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. [pdf]