Sudheendra Vijayanarasimhan      

I graduated from the Department of Computer Sciences at the University of Texas at Austin in May 2011 with a Ph.D and have since joined the Video Content Analysis team at Google, Mountain View. I obtained my B.Tech (Bachelor's) in Computer Science and Engineering at the Indian Institute of Technology, Madras.

Currciculum Vitae

Research Interests

I am primarily interested in active learning, object recognition, detection and segmentation. I worked with Prof. Kristen Grauman on active selection and annotation for recognition, segmentation and detection problems on images and videos.



  • Annotation data for MSRC (v1) dataset

  • Here's annotation (object segmentation) and timing data collected on the MSRC version 1 dataset from a large number of anonymous users using the Mechanical Turk interface. This readme file provides details on the data. Also checkout our CVPR 2009 paper for more details. These are some example segmentations that were approved.

    This data was collected with the help of Alex Sorokin who has a general interface for annotating data using Mechanical Turk.

    If you find this data useful for your research please feel free to use it and kindly use the following bibtex entry for citations. BIBTEX


  • Source code used in the Semantice Robot Vision Challenge

  • The Semantic Robot Vision Challenge (SRVC) is a workshop conducted every year where fully autonomous robots receive a text list of objects that they are to find. They use the web to automatically find image examples of those objects in order to learn visual models. These visual models are then used to identify the objects in the robot's cameras.

    I participated in the 2008 event and applied a Multiple-Instance Learning based approach for the problem. Here's the complete source code of our method. This readme file should get you started if you would like to apply our method. The source files for compiling the MIL classifier can be downloaded from here.

  • Incremental SVM

  • Here's a C++ implementation of the Incremental SVM method of Cauwenberghs and Poggio based on their MATLAB implementation .


Department of Computer Science
University of Texas at Austin
1 University Station
CSA 1.106
Austin, TX 78712-0233

Email: svnaras at cs dot utexas dot edu