List of Publications

 

 

Machine Learning and Computer Vision

 

 

.        W. Liu, C. Mu, S. Kumar, and S. F. Chang

Discrete Graph Hashing

Neural Information Processing Systems (NIPS), 2014.

[pdf]

Supplementary material can be found here.

 

.        F. X. Yu, S. Kumar, Y. Gong, and S. F. Chang

Circulant Binary Embedding

International Conference on Machine Learning (ICML), 2014.

[pdf]

Matlab code can be found here.

 

.        F. X. Yu, D. Liu, S. Kumar, T. Jebara, and S. F. Chang

pSVM for Learning with Label Proportions

International Conference on Machine Learning (ICML), 2013.

[pdf]

The supplementary file with additional proofs and experiments is here.

 

.        Y. Gong, S. Kumar, H. Rowley, and S. Lazebnik

Learning Binary Codes for High-Dimensional Data Using Bilinear Projections

IEEE Computer Vision and Pattern Recognition (CVPR), 2013.

[pdf]

 

.        A. Talwalkar, S. Kumar, M. Mohri and H. Rowley

Large-scale SVD and Manifold Learning

Journal of Machine Learning Research (JMLR), 2013.

[pdf]

 

.        Y. Gong, S. Kumar, V. Verma, and S. Lazebnik

Angular Quantization-based Binary Codes for Fast Similarity Search

Advances in Neural Information Processing Systems (NIPS), 2012.

[pdf]

 

.        J. He, S. Kumar, and S. F. Chang

On the Difficulty of Nearest Neighbor Search

International Conference on Machine Learning (ICML), 2012.

[pdf]

The supplementary file containing all the proofs is here.

NOTE: This is a slightly edited version of what is in the ICML proceedings.

 

.        W. Liu, J. Wang, Y. Mu, S. Kumar, and S. F. Chang

Compact Hyperplane Hashing with Bilinear Functions

International Conference on Machine Learning (ICML), 2012.

[pdf]

The supplementary file containing extended proofs and results is here.

 

.        J. Wang, S. Kumar, and S. F. Chang

Semi-Supervised Hashing for Large Scale Search

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2012.

[pdf]

 

.        S. Kumar, M. Mohri and A. Talwalkar

Sampling Methods for the Nystrom Method

Journal of Machine Learning Research (JMLR), 2012.

[pdf]

 

.        W. Liu, J. Wang, S. Kumar, and S. F. Chang

Hashing With Graphs

International Conference on Machine Learning (ICML), 2011.

[pdf]

 

.        A. Talwalkar, S. Kumar, M. Mohri and H. Rowley

Large-Scale Manifold Learning

Book chapter in Manifold Learning Theory and Applications. Editors: Y. Ma and Y. Fu. CRC Press, 2011.

[pdf]

 

.        S. Kumar, M. Mohri and A. Talwalkar

Ensemble Nystrom

Book chapter in Ensemble Machine Learning: Theory and Applications, Springer, 2011.

[pdf]

Modified to correct an error in the computational complexity analysis. April 2011.

 

.        A. Makadia, V. Pavlovic and S. Kumar

Baselines for Image Annotation

International Journal on Computer Vision (IJCV), 2010.

[pdf]

 

.        J. Wang, S. Kumar, and S. F. Chang

Sequential Projection Learning for Hashing with Compact Codes

International Conference on Machine Learning (ICML), 2010.

[pdf]

 

.        Z. Wang, M. Zhao, Y. Song, S. Kumar and B. Li

YouTubeCat: Learning to Categorize Wild Web Videos

IEEE Computer Vision and Pattern Recognition (CVPR), 2010.

[pdf]

 

.        J. Wang, S. Kumar, and S. F. Chang

Semi-Supervised Hashing for Scalable Image Retrieval

IEEE Computer Vision and Pattern Recognition (CVPR), 2010.

[pdf]

 

.        S. Kumar, M. Mohri and A. Talwalkar

Ensemble Nystrom Method

Neural Information Processing Systems (NIPS), 2009.

[pdf]

Modified to correct an error in the computational complexity analysis. April 2011.

 

.        S. Kumar, M. Mohri and A. Talwalkar

On Sampling-based Approximate Spectral Decomposition

International Conference on Machine Learning (ICML), 2009.

[pdf]

 

.        S. Kumar, M. Mohri and A. Talwalkar

Sampling Techniques for the Nystrom Method

Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), 2009.

[pdf]

 

.        A. Makadia, V. Pavlovic and S. Kumar

A New Baseline for Image Annotation

European Conference on Computer Vision (ECCV), 2008.

[pdf]

 

.        A. Talwalkar, S. Kumar and H. A. Rowley

Large-Scale Manifold Learning

IEEE Computer Vision and Pattern Recognition (CVPR), 2008.

[pdf]

 

.        M. Kim, S. Kumar, V. Pavlovic and H. A. Rowley

Face Tracking and Recognition with Visual Constraints in Real-World Videos

IEEE Computer Vision and Pattern Recognition (CVPR), 2008.

[pdf]

 

·        S. Kumar and H. A. Rowley

Classification of Weakly-Labeled Data with Partial Equivalence Relations

IEEE International Conference on Computer Vision (ICCV), 2007.

[pdf]

Some additional results and parts of the video and retrieval datasets used in this work can be seen here.

 

·        S. Kumar and M. Hebert

Discriminative Random Fields

International Journal of Computer Vision (IJCV), 68(2), 179-201, 2006.

[pdf]

 

·        S. Kumar, J. August and M. Hebert

Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study
Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2005.

[pdf]

This paper is an extended and revised version of the earlier work presented in Snowbird Learning Workshop, 2004.

 

·        S. Kumar

Models for Learning Spatial Interactions in Natural Images for Context-Based Classification

PhD Thesis, The Robotics Institute, School of Computer Science, Carnegie Mellon University, September 2005.

[pdf] [ps]

Revised October 2005.

 

·        S. Kumar and M. Hebert

A Hierarchical Field Framework for Unified Context-Based Classification

IEEE International Conference on Computer Vision (ICCV), 2005.

[pdf] [ps]

Revised October 2005.

 

·        C. Rother, S. Kumar, V. Kolmogorov and A. Blake

Digital Tapestry

IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2005.

[pdf]

 

·        S. Kumar and M. Hebert

            Discriminative Random Fields

            In review, International Journal of Computer Vision (IJCV), Submitted, 2004.

 

·        S. Kumar and M. Hebert

            Approximate Parameter Learning in Discriminative Fields

            Snowbird Learning Workshop, Utah, 2004.

            [pdf] [ps]

            The synthetic dataset used for learning and inference experiments can be obtained from here.

 

·        S. Kumar and M. Hebert

            Multiclass Discriminative Fields for Parts-Based Object Detection

            Snowbird Learning Workshop, Utah, 2004.

            [pdf]

 

Discriminative Fields for Modeling Spatial Dependencies in Natural Images

Advances in Neural Information Processing Systems, NIPS 16, 2004.

            [pdf] [ps]

            The binary denoising synthetic dataset used for training and testing can be obtained from here.

                       

·        S. Kumar and M. Hebert

            Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification

            IEEE International Conference on Computer Vision (ICCV), 2003.

            [pdf] [ps]

 

            Man-Made Structure Detection in Natural Images using a Causal Multiscale Random Field

            IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2003.

            [pdf]

            Some more example results and comparisons.

            The structure detection database used for training and testing can be obtained from here.

 

            An Observation-Constrained Generative Approach for Probabilistic Classification of Image Regions

            Image and Vision Computing, 21, pp. 87-97, 2003.

            [pdf] 

            A shorter version of this paper appeared in the following workshop:

 

            Probabilistic Classification of Image Regions using an Observation-Constrained Generative Approach

            ECCV Workshop on Generative Models based Vision (GMBV), 2002.

            [pdf]

 

 

Medical Imaging and Robotics

 

Path Planning with Hallucinated Worlds

      In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2004.

[pdf]

 

Design of a Vision-guided Microrobotic Colonoscopy System

            International Journal of Advanced Robotics, vol. 14, no. 2, pp. 87-104, 2000.

            [link]

 

A Fully Automatic Microrobotic Endoscopy System

Journal of Intelligent and Robotic Systems, vol. 28, pp. 325-341, 2000.

      [link]   

 

            A New Approach for Nonlinear Distortion Correction in Endoscopic Images Based on Least Squares Estimation

            IEEE Transactions on Medical Imaging, vol. 18, no. 4, pp. 345-354,1999.

            [link]

 

Real-Time Automatic Extraction of Lumen Region and Boundary from Endoscopic Images

IEE Journal of Medical & Biological Engineering & Computing, vol. 37, pp. 600-604, 1999.

            [link]

 

Technique of Distortion Correction in Endoscopic Images using a Polynomial Expansion

IEE Journal of Medical & Biological Engineering & Computing, vol. 37, no. 1, pp. 8-12, 1999.

[link]

 

A Pipelined Architecture for Image Segmentation by Adaptive Progressive Thresholding

Journal of Microprocessors and Microsystems, vol. 23, no. 8-9, pp. 493-499, 1999.

[link]

 

Online Extraction of Lumen Region and Boundary from Endoscopic Images Using a Quad Structure

IEE Conference on Image Processing and its Applications (IPA), pp. 818-822, 1999.

 

A New Technique for the Segmentation of Lumen from Endoscopic Images by Differential Region Growing

42nd Midwest Symposium on Circuits and Systems, New Mexico, 1999.

 

Development of a Microrobotic System for Intelligent Endoscopy

            2nd Scientific Meet. of Biomed. Eng. Soc., Singapore, January 1999.

 

A Computer-Based Endoscopic Image Segmentation Technique for Lumen Extraction

13th Int. Congress and Exhibition on Comp. Asst. Radio. Surgery, Paris, June, 1999.

 

Computer-Assisted Intelligent Endoscopy

13th Int. Congress and Exhibition on Comp. Asst. Radio. Surgery, Paris, June, 1999.

 

Design of a Self Guided Vehicle (SGV) with Laser Based Navigation System

National Seminar on Mechatronics, India, Madras, 1997.