UT-Austin Computer Vision Group Software


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


Deformable spatial pyramid matching

Fast, dense (pixel-level) image correspondences.  Shown to improve both accuracy and speed of SIFT Flow and Patch Match algorithms.


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.



Kernelized locality sensitive hashing (KLSH)

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


Interactive image segmentation with sufficiently strong user input

An algorithm to predict the annotation modality that is sufficiently strong for accurate segmentation of a given image (Matlab and C++).


Active learning to rank: Diverse setwise low margin selection algorithm

An algorithm for active learning of a ranking function that selects useful sets of examples to be partially ordered by an annotator, plus MTurk interface code for the cascading partial order annotation collection


Tree of Metrics (ToM)

Code and test data for Tree of Metrics (ToM), which learns a local metric at each node of a semantic taxonomy with disjoint sparsity regularization.


Semantic Kernel Forest

Code and test data for semantic kernel forest, which learns a category-specific kernel from forest of metrics learned on multiple semantic taxonomies. 


Generating taxonomies

Codes to generate a taxonomy of target categories based on WordNet or attributes, used in Tree of Metrics or Semantic Kernel Forests



Shape sharing for object segmentation

Generating object segment hypotheses based on shape sharing approach.  Written in Matlab and C++ (Matlab wrapper provided).


Crowdsourcing annotation tool to mark intervals of interest in videos

Extends VATIC tool (Vondrick et al.) to allow annotation of time intervals where an action occurs.



Max-subgraph activity detection

Action detection in continuous video.  Fast branch-and-cut approach to identify the subvolume in a video that maximizes the linear classifier's score.
  Note that CPLEX student license is available here.

Active frame selection for video label propagation

A dynamic programming-based algorithm to actively choose frames for human annotation along with label propagation with minimum expected error. Written in Matlab and tested on 64 bit Linux



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.  Note that CPLEX student license is available here.


Key-segments video object segmentation

Unsupervised segmentation of main foreground objects in video.


Asymmetric image matching

Region-to-image matching method, written in Matlab and C++ (Matlab wrapper provided).


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. 


Object-graphs

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


Multi-task feature learning


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



MIL training of object categories and MIL Bag-reweighting code



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


Incremental SVM code