Fall 2009 COMP 875 Reading Materials

Reference Books

• C. Bishop, Pattern Recognition and Machine Learning
  
  
• T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning
  
  
• R. Duda, P. Hart, and D. Stork, Pattern Classification
  
  
• D. MacKay, Information Theory, Inference, and Learning Algorithms --- book available online for download
  
  

Tutorials, surveys, introductory papers

• A Tutorial on Support Vector Machines for Pattern Recognition
  C. Burges
  Data Mining and Knowledge Discovery 2, 121-167, 1998
  
  
• A Short Introduction to Boosting
  Y. Freund and R. Schapire
  Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, September, 1999
  
  
• A Tutorial on Energy-Based Learning
  Y. LeCun, S. Chopra, R. Hadsell, M.A. Ranzato, F.J. Huang
  In Predicting Structured Data, MIT Press, 2006
  
  
• Semi-Supervised Learning Literature Survey
  X. Zhu
  University of Wisconsin Tech. Report, 2008
  
  
• An Introduction to Graphical Models
  K. Murphy
  
  
• Graphical Models
  M. I. Jordan
  Statistical Science (Special Issue on Bayesian Statistics), 19, 140-155, 2004
  
  
• An Introduction to MCMC for Machine Learning
  C. Andrieu, N. de Freitas, A. Doucet and M. Jordan
  Machine Learning, 2002
  
  

Some Computer Vision Papers

• Sharing visual features for multiclass and multiview object detection
  A. Torralba, K. P. Murphy and W. T. Freeman
  PAMI. vol. 29, no. 5, pp. 854-869, May 2007
  
  
• Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study
  J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid
  International Journal of Computer Vision, vol. 73, no. 2, June 2007, pp. 213-238
  
  
• Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification
  A. Frome, Y. Singer, F. Sha, J. Malik
  ICCV 2007
  
  
• Fast Image Search for Learned Metrics
  P. Jain, B. Kulis, and K. Grauman
  CVPR 2008
  
  
• Similarity Functions for Categorization: from Monolithic to Category Specific
  B. Babenko, S. Branson, and S. Belongie
  ICCV 2009
  
  
• Small codes and large databases for recognition
  A. Torralba, R. Fergus, Y. Weiss
  CVPR 2008
  
  
• Object Class Recognition by Unsupervised Scale-Invariant Learning
  R. Fergus, P. Perona, and A. Zisserman
  CVPR 2003
  
  
• A Discriminatively Trained, Multiscale, Deformable Part Model
  P. Felzenszwalb, D. McAllester, D. Ramanan
  CVPR 2008
  
  
• Matching Words and Pictures
  K. Barnard, P. Duygulu, N. de Freitas, D. Forsyth, D. Blei and M. Jordan
  Journal of Machine Learning Research 3 (2003) 1107-1135
  
  
• Learning Depth from Single Monocular Images
  Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng
  NIPS 2005
  
  
• Dicriminative Random Fields
  S. Kumar and M. Hebert
  IJCV 2006
  
  
• TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation
  J. Shotton, J. Winn, C. Rother, A. Criminisi
  ECCV 2006
  
  
• A Stochastic Grammar of Images
  Song-Chun Zhu and David Mumford
  Foundations and Trends in Computer Graphics and Vision Vol. 2, No 4. 2007
  
  
• Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework
  L.-J. Li, R. Socher and L. Fei-Fei
  CVPR 2009
  
  
• Multi-Level Active Prediction of Useful Image Annotations for Recognition
  S. Vijayanarasimhan and K. Grauman
  NIPS 2008
  
  
• Condensation -- Conditional density propagation for visual tracking
  M. Isard and A. Blake
  IJCV, 1998
  
  

Some Medical Imaging Papers

• Discriminative learning for deformable shape segmentation: A comparative study
  J. Zhang, S. Kevin Zhou, D. Comaniciu, and L. McMillan
  ECCV 2008
  
  
• A probabilistic, hierarchical, and discriminant (PHD) framework for rapid and accurate detection of deformable anatomic structure
  S. Kevin Zhou, F. Guo, J.H. Park, G. Carneiro, J. Jackson, M. Brendel, C. Simopoulos, J. Otsuki, and D. Comaniciu
  ICCV 2007
  
  
• Automatic Fetal Face Detection From Ultrasound Volumes Via Learning 3D and 2D Information
  S. Feng, S. Kevin Zhou, S. Good, and D. Comaniciu
  CVPR 2009
  
  
• Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models
  Tu Z., Narr K.L., Dollar P., Dinov I., Thompson P.M., Toga A.W.
  Transactions on Medical Imaging, April 2008
  
  
• Auto-context and Its Application to High-level Vision Tasks and 3D Brain Image Segmentation
  Z. Tu and X. Bai
  PAMI, 2009
  
  
• Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease through Automated Hippocampal Segmentation
  Jonathan H. Morra, Zhuowen Tu, Liana G. Apostolova, Amity Green, Arthur W. Toga, and Paul M. Thompson
  IEEE Trans. on Medical Imaging, 2009
  
  
• Joint Sulcal Detection on Cortical Surfaces with Graphical Models and Boosted Priors
  Y. Shi, Z. Tu, A. L. Reiss, R. Dutton, A. Lee, A.M. Galaburda, I. Dinov, P. Thompson, A. Toga
  IEEE Trans. on Medical Imaging, 2009
  
  
• Data-driven Population Analysis through Mixture-Modeling
  M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland
  IEEE Transactions on Medical Imaging, 2009
  
  
• Discovering Structure in the Space of Activation Profiles in fMRI
  D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland
  MICCAI 2008
  
  
• Classification of Tensors and Fiber Tracts Using Mercer-Kernels Encoding Soft Probabilistic Spatial and Diffusion Information
  R. Neji, N. Paragios, G. Fleury, J-P. Thiran & G. Langs
  CVPR 2009