Sudheendra Vijayanarasimhan
Currciculum Vitae
Research Interests
I am interested in Machine Learning, Computer Vision, Object Recognition and Categorization. I'm currently working with Prof. Kristen Grauman on actively predicting both the manual effort required to obtain an annotation and the information gain associated with the annotation in image categorization.Publications
"Cost Sensitive Active Visual Category Learning"
S. Vijayanarasimhan and K. Grauman, The Learning Workshop, Clearwater, FL, April 2009
[Abstract]
"What's It Going to Cost You?: Predicting Effort vs. Informativeness for Multi-Label Image Annotations"
S. Vijayanarasimhan and K. Grauman, in CVPR 2009 Abstract
[PDF]
Multi-Level Active Prediction of Useful Image Annotations for Recognition
S. Vijayanarasimhan and K. Grauman, in NIPS 2008 Abstract
[PDF]
[Supplementary material]
[SLIDES]
[TR]
[Project page]
Keywords to Visual Categories: Multiple-Instance Learning for Weakly Supervised Object Categorization
S. Vijayanarasimhan and K. Grauman in CVPR 2008 Abstract
Data
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
Code
Source code used in the Semantice Robot Vision Challenge
Incremental SVM
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.
Links
Friends
Vinodh Muralidaran Murtaza Deepak Shyamnath Vishal Anirudh Anirudh Prasad Arunachalam Srikanth SidContact
Address:
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