Introduction [pdf]

DHS Textbook Chapter 1

DIP Gonzalez and Woods, Textbook, Chapter 12, Object Recognition

Paper: Anil K. Jain, etc., "Statistical Pattern Recognition: A Review," IEEE Tran. on Pattern Analysis and Machine Intelligence, vol 22, No 1, Jan. 2000.

Bayesian Decision Theory

[pdf]

DHS Chap 2

Image retrieval: Current techniques, promising directions and open issues
Y Rui, TS Huang, SF Chang - Journal of Visual Communication and Image Representation, 1999 [link]

Discriminant-based classifiers for Gaussians

[pdf]

Homework #1

due 2005-09-21

Solution to HW#1

Bayesian Classifiers (Gaussian)

Missing Features

[pdf]

DHS Chap 2.6

DHS 2.10

ML Parameter Estimation

Expectation Maximization

[pdf]

DHS 3.2, 3.9

Reference Book HTF 8.5

Homework #2

due 2005-09-28

Solution to HW#2

Expectation Maximization for missing features

Bayesian Parameter Estimation

Application: Bayesian Face Detection

[pdf]

DHS 3.2

DHS 3.3-3.6

H. Schneiderman and T. Kanade, Probabilistic modeling of local appearance and spatial relationships for object recognition, in IEEE Conference on Computer Vision and Pattern Recognition, 6, 1998. [pdf file]

Problem of High Dimensionality

Nonparametric Estimation

[pdf]

G.V. Trunk, “A Problem of Dimensionality: a Simple Example,” IEEE Trans-PAMI, July 1979. [pdf file]

DHS 4.1-4.3

Homework #3

due 2005-10-12

Updated solution to HW#3

Nonparametric Estimation

Paper: Bayesian Image Classifiers with VQ and Parzen Window

[pdf]

DHS 4.1-4.3

A. Vailaya, M. Figueiredo, A. Jain, and HJ Zhang, "A Bayesian Framework for Semantic Classification of Outdoor Vacation Images," IEEE Trans. Image Processing, Vol. 10, No. 1, pp. 157-172, Jan. 2001. [pdf file]

Nonparametric Estimation

Nearest Neighbors

[pdf]

DHS 4.1-4.3

DHS 4.4-4.6

Nearest Neighbors

Error Bound

LVQ

[pdf]

DHS 4.4-4.6

Reference Book HTF 13.1-13.3

Homework #4

due 2005-10-19

Solution to HW#4

Linear Discriminant Functions

[pdf]

Linear Discriminant Functions with relaxation

[pdf]

Midterm Exam

1pm -2:30 pm (90 mins)

Discrimant functions in high dimension

Gradient Chain Rule

[pdf]

Homework #5

due 2005-11-2

Solution to HW#5

2005-10-31

(No Class)

2005-11-2 (long lecture, start at 12:20pm)

SVM

[pdf]

DHS Chap 5.11

Paper: Christopher J.C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery 2, 121-167, 1998. [pdf]

Homework #6

due 2005-11-16

Solution to HW#6

2005-11-7 (University Holiday, No Class)

Course Project due 2005-12-12

Project Description

data set download
(Large! 72.2MB)

SVM part II

(pdf file)

Guest Lecture on SVM based real-time video semantic filtering (Dr. Ching-Yung Lin)

(pdf file)

paper (TBA)

Demo of SVM tool (libsvm)

Analysis of classification algorithms

(pdf file)

DHS Chap 9.3 and 9.5

Paper: Yoav Freund and Robert E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," In Computational Learning Theory: Eurocolt ’95, pages 23–37. Springer-Verlag, 1995. [link]

Homework #7

due 2005-11-30

(changed to 2005-12-5)

Solution to HW#7

svm demo

(demo package)

2005-11-23
(regular class time, start at 1:10pm)

Boosting, Fusiong of Classifiers

(pdf file)

DHS Chap. 9.5 and 9.7

Paper: Paul Viola and Michael Jones, "Rapid object detection using a boosted cascade of simple features," CVPR, 2001.(pdf)

R. Yan, J. Yang, and A. Hauptmann, “Learning Class-Dependent Weights in Automatic Video Retrieval,” ACM Multimedia 2004. (pdf)

Feature dimension reduction:
PCA, ICA

(pdf file)

AdaBoost

ICA

LDA

(pdf file)

Clustering

(pdf file)

Homework #8

due 2005-12-12

Solution to HW#8

Graph Based Clustering and Image Segmentation

[link to pdf file of graph cuts part]

Link to J. Shi's CVPR 04 tutorial

[tutorial web site]

Review

[pdf file]

Solutions to Chap 10 Prob. 25-28

Final Exam, 1:10-3pm, Mudd Rm 644
(new location)