Lecture #  Date  Topics  Readings  Assignments  Notes 
1  20050907  Introduction [pdf]

DHS Textbook Chapter 1 DIP Gonzalez and Woods, Textbook, Chapter 12, Object Recognition



2  20050912  Bayesian Decision Theory [pdf] 
DHS Chap 2 Image retrieval: Current techniques, promising directions and open issues 


3  20050914  Discriminantbased classifiers for Gaussians [pdf] 
DHS Chap 2.52.6 
due 20050921 
Matlab demo script #1 
4  20050919  Bayesian Classifiers (Gaussian) Missing Features [pdf] 
DHS Chap 2.6 DHS 2.10 


5  20050921  ML Parameter Estimation Expectation Maximization [pdf]

DHS 3.2, 3.9 Reference Book HTF 8.5 
due 20050928 
data set for Hw#2 
6  20050928  Expectation Maximization for missing features Bayesian Parameter Estimation Application: Bayesian Face Detection [pdf]

DHS 3.2 DHS 3.33.6 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] 


7  20051003  Problem of High Dimensionality Nonparametric Estimation [pdf]

G.V. Trunk, “A Problem of Dimensionality: a Simple Example,” IEEE TransPAMI, July 1979. [pdf file] DHS 4.14.3 
due 20051012 
solutions for Prob. 47 and 48 of Chapter 3 
8  20051005  Nonparametric Estimation Paper: Bayesian Image Classifiers with VQ and Parzen Window [pdf]

DHS 4.14.3 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. 157172, Jan. 2001. [pdf file] 


9  20051010  Nonparametric Estimation Nearest Neighbors [pdf] 
DHS 4.14.3 DHS 4.44.6 


10  20051012  Nearest Neighbors Error Bound LVQ [pdf] 
DHS 4.44.6 Reference Book HTF 13.113.3 
due 20051019 

11  20051017  Linear Discriminant Functions [pdf] 
DHS 5.15.4 


12  20051019  Linear Discriminant Functions with relaxation [pdf]

DHS 5.35.6 


20051024  Midterm Exam 1pm 2:30 pm (90 mins)



13  20051026  Discrimant functions in high dimension Gradient Chain Rule [pdf] 
DHS 5.65.8 
due 2005112 

20051031 (No Class) 



14long  2005112 (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,
121167, 1998. [pdf] 
due 20051116 

2005117 (University Holiday, No Class) 


Course Project due 20051212 data set download 

2005119, 1114 (No Class) 



15long  2005 1116 (long lecture, start at 12:20pm)  SVM part II (pdf file) Guest Lecture on SVM based realtime video semantic filtering (Dr. ChingYung Lin) (pdf file) 
paper (TBA) 


16long  2005 1121 (long lecture, start at 12:20pm)  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 decisiontheoretic generalization of online learning and an application to boosting," In Computational Learning Theory: Eurocolt ’95, pages 23–37. SpringerVerlag, 1995. [link] 
due 20051130 (changed to 2005125) 
svm demo 
17  20051123 
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 ClassDependent Weights in Automatic Video Retrieval,” ACM Multimedia 2004. (pdf) 


18  20051128  Feature dimension reduction: (pdf file) 
DHS Chap. 3.8, 10.13 


19  20051130  AdaBoost ICA LDA (pdf file) 
Aapo Hyvärinen and Erkki Oja, “Independent Component Analysis:
Algorithms and Applications,” Neural Networks, 13(45):411430, 2000
[link]



20  2005125  Clustering (pdf file) 
DHS Chap.10.2,10.3, 10.4, 10.7, 10.9 
due 20051212 

21  2005127 
Graph Based Clustering and Image Segmentation [link to pdf file of graph cuts part] 
Link to J. Shi's CVPR 04 tutorial 


22  20051212  Review [pdf file] 
Solutions to Chap 10 Prob. 2528 
Course Project due  
20051216 Friday  Final Exam, 1:103pm, Mudd Rm 644 
