Computer Science & Engineering Department
CS 479/679 Pattern Recognition (Spring 2016)
Meets: MW 1:00pm - 2:15pm (SEM 257)
Dr. George Bebis
- Office: SEM 241/242
- Office Hours: MW 2:30pm - 4:00pm and/or by appointment.
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd edition, Wiley-Interscience. ISBN 0-471-05669-3 Errata
Optional Texts (not required):
T. Hastie et al., The Elements of Statistical Learning, Springer-Verlag, 2001.
K. Murphy, Machine Learning: A probabilistic Perspective, MIT Press, 2012.
IEEE Computer Vision and Pattern Recognition (CVPR)
International Conference of Pattern Recognition (ICPR)
Useful Mathematics and Statistics resources
This course will introduce the fundamentals of statistical pattern recognition.
First, we will focus on generative methods such as those based on Bayes
decision theory and related techniques of parameter estimation and density
estimation. Next, we will focus on discriminative methods such as
nearest-neighbor classification and support vector machines. Methods of pattern
recognition are useful in many applications such as information retrieval, data
mining, document image analysis and recognition, computational linguistics,
forensics, biometrics and bioinformatics. In this course, we will emphasize
computer vision applications.
Course Outline (tentative)
Bayesian Decision Theory
Maximum Likelihood Estimation
Linear Discriminant Functions
Support Vector Machines (SVMs)
Expectation-Maximization (EM) Algorithm
302 with a "C" or better; MATH/STAT 352. Credit hours: 3.0
Exams and Assignments
Grading will be based on two exams, several quizzes, and 4-5 programming assignments and a paper presentation. Details are provided below:
Homework problems will be assigned but will NOT be collected for grading. Homework solutions will be made available for each assignment.
There will be 2 exams: a midterm and a final. The material covered in the exams will be drawn from the lectures and the homework.
There will be several quizzes during the semester which will be announced at least one class period in advance.
There will be 4-5 programming assignments which will be done in groups of two students. For each assignment, the group would need to turn in a report; details will be provided for each assignment.
Graduate students will be required to present a research paper to the rest of the class. Each presentation should be professional as if it was presented in a formal conference (i.e., slides/projector). Students can choose a paper from those listed on the course’s webpage or can suggest other papers to the instructor for possible presentation. Presentations should be 20 minutes long and will take place at the end of the semester.
Lecture slides, assignments, and other useful information will be posted on the course.s web page.
Regular attendance is highly recommended. If you miss a class, you are responsible for all material covered or assigned in class.
A missed exam may be made up only if it was missed due to an extreme emergency.
No late assignments will be accepted unless there is an extreme emergency.
No incomplete grades (INC) will be given in this course unless there is an extreme emergency.
Lectures and Reading Assignments
- Introduction to Pattern Recognition
- Review of Probability
- Review of Linear Algebra
- Bayesian Decision Theory
- Bayesian Decision Theory - Case Studies
- Bayesian Networks
- E. Charniak, Bayesian Networks without Tears, AI Magazine, 1991.
- A Brief Introduction to Graphical Models and Bayesian Networks
- D. Heckerman, A Tutorial on Learning with Bayesian Networks , Technical Report, Microsoft, 1995.
- W. Buntine, A guide to the literature on learning probabilistic networks from data , IEEE Transactions of Knowledge and Data Engineering, vol. 8, no. 2, pp. 195-210, 1996.
- Maximum Likelihood (ML) Estimation
- Bayesian Estimation
- Dimensionality Reduction (with case studies)
- M. Turk and A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
- Daniel L. Swets, John (Juyang) Weng Using Discriminant Eigenfeatures for Image Retrieval , IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 18, No 8 , pp. 831-836, 1996.
- A. Martinez and A. Kak PCA versus LDA, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 23, No 2, pp. 228 -233, 2001.
- C. Burges, Dimension Reduction: A Guided Tour, Microsoft Research Tech Report MSR-TR-2009-2013.
- Feature Selection (with case studies)
- Feature Selection Algorithms - A Brief Guide
- Jain, A.K.; Duin, P.W.; Jianchang Mao, Statistical pattern recognition: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 22, no 1, pp. 4-37, Jan. 2000.
- H. Liu et al., Feature Selection: An Ever Evolving Frontier in Data Mining, Fourth Workshop on Feature Selection in Data Mining, 2010.
- Isabelle Guyon and Andre Elisseeff, An Introduction to Variable and Feature Selection, Journal of Machine Learning Research 3 (2003) 1157-1182.
- Z. Sun, G. Bebis, X. Yuan, and S. Louis, Genetic Feature Subset Selection for Gender Classification: A Comparison Study, IEEE Workshop on Applications of Computer Vision, pp. 165-170, Orlando, December 2002.
- Z. Sun, G. Bebis, and R. Miller, Object Detection Using Feature Subset Selection, Pattern Recognition, vol. 37, pp. 2165-2176, 2004.
- G. Bebis, A. Gyaourova, S. Singh, and I. Pavlidis, Face Recognition by Fusing Thermal Infrared and Visible Imagery, Image and Vision Computing, vol. 24, no. 7, pp. 727-742, 2006.
- Linear Discriminant Functions
- Support Vector Machines
- C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, Kluwer Academic Publishers, 1998.
- M. Pontil and A. Verri, Support vector machines for 3D object recognition, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 20, no. 6, pp. 637-646,
- A. Mojan, C. Papageorgiou and T. Poggio, Example-based object detection in images by components, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 23, no. 4, pp. 349-361, 2001.
- B. Moghaddam and M. Yang, Gender Classification with SVM, IEEE Transactions on PAMI, vol. 24, no. 5, 2002.
- EM Algorithm
- EM Case Studies
- S. McKenna, Y. Raja, and S. Gong, Tracking color objects using adaptive mixture models, Image and Vision Computing, vol. 17, pp. 225-231, 1999.
- C. Stauffer and E. Grimson, Adaptive background mixture models for real-time tracking, IEEE Computer Vision and Pattern Recognition Conference, Vol. 2, pp. 246-252,
- Final Exam Review
Hongda Tian et al., Single Image Smoke Detection, ACCV, 2014. (Jeff Soriano - April 13, 2016)
Paul Blondel et al., Human Detection in Uncluttered Environments: from Ground to UAV View (Seyed Pourya Hoseini Alinodehi - April 18, 2016)
Faisal Ahmed et al., Classification of crops and weeds from digital images: A support vector machine approach, Crop Protection, 2012. (Jiwan Bhandari - April 18, 2016)
Tom Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, 2006. (Nathan Wiseman - April 20, 2016)
Hidenori Maruta et al., A Novel Smoke Detection Method Using Support Vector Machine, IEEE Tencon 2010. (Tuan Dzung Le - April 20, 2016)
Sebastian Haug et al., Plant Classification System for Crop /Weed Discrimination without Segmentation, Applications of Computer Vision (WACV), 2014 IEEE Winter Conference on. (Chad Adams - April 20, 2016)
Xi Huang et al., An Intelligent Multifeature Statistical Approach for the Discrimination of Driving Conditions of a Hybrid Electric Vehicle, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 12, NO. 2, JUNE 2011. (Yuan Sun - April 25, 2016)
Jose Bins and Bruce Draper, Feature Selection from Huge Feature Sets, ICCV 2001. (Ahmet Aksoy - April 25, 2016)
Papers for Student Presentations
Hongda Tian et al., Single ImageSmokeDetection, ACCV, 2014.
Simone Calderara, Paolo Piccinini, and Rita Cucchiara Vision based smoke detection system using image energy and color information, Machine Vision and Applications (2011) 22: 705-719.
Chengjiang Long et al. Transmission: A New Feature for Computer Vision Based Smoke Detection, ICI 2010, Part I, LNAI 6319, pp. 389–396, 2010.
Hongda Tian et al. Smoke detection in video: an image separation approach, Int Journal of Computer Vision, 106 (2), 192-209.
Turgay Celik et al. FIRE AND SMOKE DETECTION WITHOUT SENSORS: IMAGE PROCESSING BASED APPROACH, 15th European Signal Processing Conference (EUSIPCO 2007).
Hidenori Maruta et al., A Novel Smoke Detection Method Using Support Vector Machine, IEEE Tencon 2010.
JunOh Park et al., Wildfire Smoke Detection Using SpatioTemporal Bag-of-Features of Smoke, Applications of Computer Vision (WACV), 2013 IEEE Workshop on.
Pietro Morerio et al., EARLY FIRE AND SMOKE DETECTION BASED ON COLOUR FEATURES AND MOTION ANALYSIS, IEEE ICIP 2012.
- Xi Huang et al., An Intelligent Multifeature Statistical Approach for the Discrimination of Driving Conditions of a Hybrid Electric Vehicle, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 12, NO. 2, JUNE 2011.
- Sancho McCann and David G. Lowe, Local Naive Bayes Nearest Neighbor for Image Classification, CVPR, 2012.
- M. Jones and J. Rehg, Statistical Color Models with Application to Skin Detection, International Journal of Computer Vision 46(1), 81-96, 2002.
- Oren Boiman et al., In Defense of Nearest-Neighbor Based Image Classification, CVPR 2008.
- Nemanja Petrovi et al., Object Tracking Using Naive Bayesian Classifiers CIVS 2008, LNCS 5259, pp. 775.784, 2008.
- Daniel Keren, Painter Identification Using Local Features and Naive Bayes , ICPR 2002.
- Martin Godec, Christian Leistner, Amir Saffari, Horst Bischof, On-line Random Naive Bayes for Tracking, ICPR 2010.
- S. Mahamud, M. Hebert, and J. Shi, Object Recognition Using Boosted Discriminants, CVPR 2001.
- H. Schneiderman and T. Kanade, A statistical method for 3D object detection applied to faces and cars, CVPR 2000.
- Tom Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, 2006.
Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
Page created and maintained by:
Dr. George Bebis