Spring 2011 - CIS 8590.002

Visual Information Analysis

Basic Information

·  Lecture Time: Mon 5:30-8:00pm, TL 0001B

·  Instructor: Haibin Ling | Wachman Hall, Room 305 | 215-204-6973 | hbling AT temple.edu

·  Office Hours: Mon 3:30-5:30pm, or by appointment

·  Syllabus: PDF

Date | People

Topics and Materials

(Tentative, up to change)



Week 1

General introduction. (Slide 1, 2)

·  Background

·  Topics in visual data analysis

·  Applications

·  Related fields
·  Image formation


·  Szeliski's book, Ch. 1-2



Week 2

Background - Elementary Math and Statistics (Slides)

·  Elementary probability and statistics

·  Linear analysis (PCA, LDA, etc.)

·  Graph algorithms (HMM, MRF, etc.)


Materials: (Slides)

·  Szeliski's book, Appendix A-C

·  A Global Geometric Framework for Nonlinear Dimensionality Reduction, Joshua B. Tenenbaum, Vin de Silva, John C. Langford, Science.

·  Nonlinear Dimensionality Reduction by Locally Linear Embedding, Sam T. Roweis, Lawrence K. Saul, Science.



Week 3

Background - Optimization (Slides)

·  Basic idea and linear optimization

·  Convex optimization


·  Convex optimization by Boyd and Vandenberghe.

·  Additive logistic regression: a statistical view of boosting, J. Friedman, T. Hastie, and R. Tibshirani, The Annals of Statistics 2000.




Week 4



Paper selection due!


·  Clustering, perceptual grouping, segmentation



·  Szeliski's book, Ch. 5

Paper presentation:

·  Normalized Cuts and Image Segmentation, by J. Shi and J. Malik, PAMI 2000

·  Graph Cut based Inference with Co-occurrence Statistics, by L. Ladicky, C. Russell, P. Kohli, P. H.S. Torr, ECCV 2010


Additional Readings:

·  Learning to Combine Bottom-Up and Top-Down Segmentation, A. Levin and Y. Weiss, IJCV 2009.

·  "Stochastic relaxation, gibbs distributions, and the bayesian restoration of images",  S.Geman and D.Geman. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721--741, 1984.



Week 5

Features and Representation

·  What are features?

·  Invariant features and representations



·  Szeliski's book, Ch. 4


Paper presentation

·  [Presenter: Kristiyan Georgiev] Local Grayvalue Invariants for Image Retrieval, C.Schmid & R.Mohr, PAMI, 19(5), 530-535, 1997.

·  [Presenter: Yunsheng Wang] Distinctive image features from scale-invariant keypoints," D. Lowe, IJCV 2004.




Week 6

Matching, Alignment, and Registration

·  Feature matching

·  Surface matching

·  Image matching


·  Szeliski's book, Ch. 6


Paper presentation:

· [Presenter: Wei Chang] A new point matching algorithm for non-rigid registration, H. Chui and A. Rangarajan, Computer Vision and Image Understanding (CVIU), 89:114-141, 2003.

·  Alignment by Maximization of Mutual Information, by P. Viola and W. M. Wells III, 1997

Additional Readings:

·  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations, by F. Bookstein, PAMI 1989, Vol 11, No 6.




Spring Break!




Week 7



Shape (Slides)

·  Shape space - Procrustes analysis

·  Shape matching - TPS, RANSAC

·  Shape classification - shape context


·  Szeliski's book, Ch. 14


Paper presentation:

·  [Presenter: Richard Hart] A Novel Riemannian Framework for Shape Analysis of 3D Objects, Sebastian Kurtek, Eric Klassen, Anuj Srivastava, Zhaohua Ding, CVPR, 2010

·  [Presenter: Liang Du] Patchwork of Parts Models for Object Recognition, Y. Amit, A.PTrouve, IJCV 2007.


Additional Readings:

·  Shape Matching and Object Recognition Using Shape Contexts, S. Belongie, J. Malik and J. Puzicha, PAMI, 24(4):509-522, April 2002.

·  Eigenfaces for recognition. Journal of Cognitive Neuroscience, Turk, M. & Pentland, A. (1991) 3, 71-86

·  Robust Face Recognition via Sparse Representation, J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma, PAMI'09

·  Attribute and Simile Classifiers for Face Verification, by N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, ICCV'09
·  Lambertian Reflectance and Linear Subspaces, IEEE Trans. on Pattern Analysis and Machine Intelligence, 25(2):218-233, (2003). R. Basri and D. Jacobs.




Week 8



Recognition of faces, category, and scenes (Slides)

·  Face recognition

·  Bag-of-words


·  Szeliski's book, Ch. 14

·  Tutorial by L. Fei-fei, R. Fergus, and A. Torralba


Paper presentation:

·  [Presenter: Haitao Lang] Modeling the shape of the scene: a holistic representation of the spatial envelope, Aude Oliva, Antonio Torralba, International Journal of Computer Vision, Vol. 42(3): 145-175, 2001.


Additional Reading:

·  Nonparametric scene parsing: label transfer via dense scene alignment. C. Liu, J. Yuen, and A. Torralba. CVPR, 2009.

·  Multimodal semi-supervised learning for image classification, Matthieu Guillaumin, Jakob Verbeek, Cordelia Schmid, CVPR 2010.




Week 9


Project proposal


Project proposal

·  Five minutes per student


Object detection

·  Human detection

·  Face detection

·  General object detection

Paper presentation:

·  [Presenter: Le Shu] Histograms of Oriented Gradients for Human Detection, N. Dalal and B. Triggs, CVPR, 2005.

·  [Presenter: Chengliang Wang] Object Detection with Discriminatively Trained Part Based Models, P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, PAMI, Vol. 32, No. 9, 2010


Additional Reading:

·  Robust Real-time Object Detection, P. Viola and M. Jones, IJCV 2002.



Week 10

Video analysis - Tracking

·  Survey of visual tracking



·  A. Yilmaz, O. Javed, and M. Shah, Object Tracking: A Survey, ACM Journal of Computing Surveys, Vol. 38, No. 4, 2006.

Paper presentation:

·  CONDENSATION - conditional density propagation for visual tracking, M. Isard and A. Blake, IJCV 1998.

·  [Presenter: Yi Wu] C.H. Kuo, C. Huang, and R. Nevatia, "Multi-target tracking by on-line learned discriminative appearance models," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.


Additional Reading:

·  Tracking the Invisible: Learning Where the Object Might be, H. Grabner, J. Matas, L. Van Gool, P. Cattin, CVPR 2010

·  Visual Tracking with Online Multiple Instance Learning, B. Babenko, M.-H. Yang, S. Belongie, CVPR'09.

·  Robust Visual Tracking using L1 minimization, X. Mei and H. Ling, ICCV'09



Week 11

Video analysis - Activity Understanding (slides)

·  Features and representation

·  Low and high level models



·  P. Turaga, R. Chellappa, V.S. Subrahmanian and O. Udrea, "Machine Recognition of Human Activities: A Survey", IEEE Transactions on Circuits and Systems for Video technology, pp. Vol. 18, pp. 1473 ¨C 1488, Nov. 2008


Paper presentation:

· [Presenter: Shirley Huang] On space-time interest points, I Laptev, IJCV 2005.
· [Presenter: Shirley Huang] Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words, J. C. Niebles, H. Wang and L. Fei-Fei, IJCV 2008.


Additional Reading:

·  Behavior Recognition via Sparse Spatio-Temporal Features, P Dollar, V Rabaud, G Cottrell, S Belongie, VS-PETS, 2005.

·  Actions as space-time shapes, Gorelick, L., Blank, M., Shechtman, E., Irani, M., and Basri, R. PAMI 2007.



Week 12

Medical imaging analysis

·  Introduction

Paper presentation:

· [Presenter: Ross Creed] Non-rigid Image Registration with Uniform Spherical Structure Patterns, S. Liao and A. C.S. Chung, IPMI 2009.

· [Presenter: Erkang Cheng] Fast Approximate Energy Minimization via Graph Cuts, Y. Boykov, O. Veksler, R. Zabih, PAMI, 2001.


Additional Reading:

· Bayesian Registration via Local Image Regions: Information, Selection and Marginalization, M. Towes and W. Wells III, IPMI 2009.

· Random walks for image segmentation, Leo Grady, PAMI, 2006.
· Statistical models of appearance for computer vision'' by Cootes and  Taylor, 2004

· Fast Automatic Heart Chamber Segmentation from 3D CT Data Using Marginal space Learning and Steerable Features, Y. Zheng, A. Barbu, B. Georgescu, M. Scheuering, D. Comaniciu, ICCV'07



Week 13

Project presentation

Presenters: Kristiyan Georgiev, Chenglaing Wang, Le Shu, Shirley Huang



Week 14

Project presentation

Presenters: Richard Hart, Liang Du, Yunsheng Wang & Wei Chang, Ross Creed




Project report due!