Fall 2007

1. Introduction to Statistical Pattern Recognition and Probalistic Models

• Anil K. Jain, etc., "Statistical Pattern Recognition: A Review," IEEE Tran. on Pattern Analysis and Machine Intelligence, vol 22, No 1, Jan. 2000. [link]
• Gonzalez and Woods, Digital Image Processing, 2nd edition, Prentice Hall, 2001 (Chapter 12, Object recognition)
• Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1989. (Chapter 9.14)
• Probability Refresher, notes for EE E6880, Statistical Pattern Recognition, Vittorio Castelli, Spring 2002 [link]
• Image Classification, Digital Image Processing Textbooks, Gonzalez and Woods Chap 12 or Anil Jain Chap 9.14
• A K Jain, etc., "Data clustering: A Review," ACM Computing Surveys, vol. 31, no. 3, Sept. 1999. [link]
• Jeff Bilms, "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models," ICSI TR-97-021 April 1998. [link]


2. Feature Extraction and Representation
• Manjunath, B.S.; Ohm, J.-R.; Vasudevan, V.V.; Yamada, A., "Color and texture descriptors," IEEE Transactions on Circuits and Systems for Video Technology, Volume: 11 Issue: 6 , Page(s): 703 -715, June 2001. [link]
• Jing Huang, S. Ravi Kumar, Mandar Mitra, Wei-Jing Zhu, and Ramin Zabih. Image indexing using color correlograms. In IEEE Conference on Computer Vision and Pattern Recognition, pages 762--768, 1997
• David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110. [PDF, demo tool, UCLA-software]
• Visual words
  Sivic, J. and Zisserman, A. Video Google: A Text Retrieval Approach to Object Matching in Videos, Proceedings of the International Conference on Computer Vision (2003)
  
  Audio Features:
  
• Liu, Z., Wang, Y., and Chen, T. Audio Feature Extraction and Analysis for Scene Segmentation and Classification. J. VLSI Signal Process. Syst. 20, 1-2 (Oct. 1998), 61-79.
• Wold, E., Blum, T., Keislar, D., and Wheaton, J. 1996. Content-Based Classification, Search, and Retrieval of Audio. IEEE MultiMedia 3, 3 (Sep. 1996), 27-36.
• Bernard Gold, Nelson Morgan, "Speech and audio signal processing : processing and perception of speech and music", Wiley, 2000.
• Jonathan Foote, An overview of audio information retrieval, Multimedia Systems 1999


3. Content-Based Image Search Systems
• Rui, Y., Huang, T.S. and Chang, S.F. Image retrieval: current techniques, promising directions and open issues. Journal of Visual Communication and Image Representation, 10 (4). 39-62.
• Smeulders, A.W.M.; Worring, M.; Santini, S.; Gupta, A.; Jain, R., "Content-based image retrieval at the end of the early years," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.22, no.12, pp.1349-1380, Dec 2000
• M. Flickher, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovicand D. Steele, and P. Yanker. Query by image and video content: The QBIC system. In IEEE Computer, volume 38, pages 23-31, 1995. (link)
• Charles E. Jacobs, Adam Finkelstein, David H. Salesin, "Fast multiresolution image querying," SIGGRAPH 1995. (link)
• John R. Smith, Shih-Fu Chang. VisualSEEk: a Fully Automated Content-Based Image Query System. In ACM Multimedia, Boston, MA, November 1996. (link)
• Rubner, Y., Tomasi, C., and Guibas, L. J. 2000. The Earth Mover's Distance as a Metric for Image Retrieval. Int. J. Comput. Vision 40, 2 (Nov. 2000), 99-121. (pdf)
• NIST TRECVID Video Retrieval Evaluation (link)
• The CLEF Cross Language Image Retrieval Track (ImageCLEF)


4. Web Image Search
• J. R. Smith and S.-F. Chang, "Visually Searching the Web for Content," IEEE Multimedia Magazine, Summer, Vol. 4 No. 3, pp.12-20, 1997. (pdf file)
• Xin-Jing Wang, Lei Zhang, Feng Jing, Wei-Ying Ma: AnnoSearch: Image Auto-Annotation by Search. CVPR (2) 2006: 1483-1490


5. Media Copy/Duplicate Detection
• "A Quick Search Method for Audio and Video Signals Based on Histogram Pruning " Kunio Kashino, Takayuki Kurozumi, and Hiroshi Murase (NTT), IEEE Trans. on Multimedia, Sept. 2003. [link]
• Chum, O. , Philbin, J. , Isard, M. and Zisserman, A., Scalable Near Identical Image and Shot Detection, Proceedings of the International Conference on Image and Video Retrieval (2007)
• Xianfeng Yang; Ping Xue; Qi Tian, "Automatically Discovering Unknown Short Video Repeats," Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on , vol.1, no., pp.I-1265-I-1268, 15-20 April 2007.
• Ke, Y., Sukthankar, R. and Huston, L., Efficient near-duplicate detection and sub-image retrieval. in ACM Multimedia, (2004).
  
6. Image Annotation
• A. Vailaya, A. Jain, and HJ Zhang, "On Image Classification: City Images vs. Landscapes," Pattern Recognition Journal, Pattern Recognition, Vol. 31, No. 12, pp. 1921-1935, 1998. [link]
• 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. [link]
• K. Barnard and D. A. Forsyth. Learning the semantics of words and pictures. In International Conference on Computer Vision, pages II:408–415, 2001. [link]
• "Relevance-based language models", V. Lavrenko and W.B. Croft, ACM SIGIR 2001 [link]
• "Automatic Image Annotation and Retrieval using Cross-Media Relevance Models", J. Jeon, V. Lavrenko, R. Manmatha, SIGIR'03. [link]
• R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2003.
• Winn, J., Criminisi, A., and Minka, T. 2005. Object Categorization by Learned Universal Visual Dictionary. In Proceedings of the Tenth IEEE international Conference on Computer Vision - Volume 2 (October 17 - 20, 2005). ICCV. IEEE Computer Society, Washington, DC, 1800-1807.
• 1. Naphade, M., Smith, J.R., Tesic, J., Chang, S.F., Hsu, W., Kennedy, L., Hauptmann, A. and Curtis, J. Large-scale concept ontology for multimedia. IEEE MultiMedia Magazine, 13 (3). 86-91.



7. Boosting for Image Retrieval and Object Detection
• 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]
• K. Tieu and P.Viola, "Boosting Image Retrieval," CVPR 2000. [link]
• Paul Viola and Michael Jones, "Rapid object detection using a boosted cascade of simple features," CVPR, 2001. [link]
  


8. Video Structure Parsing and Maximum Entropy Model
• D. Beeferman and A. Berger and J. D. Lafferty, "Statistical Models for Text Segmentation," Machine Learning, p. 177-210, vol. 34, 1999. [link]
• Winston Hsu, Shih-Fu Chang, Chih-Wei Huang, Lyndon Kennedy, Ching-Yung Lin, Giridharan Iyengar. Discovery and Fusion of Salient Multi-modal Features towards News Story Segmentation. In IS&T/SPIE Symposium on Electronic Imaging: Science and Technology - SPIE Storage and Retrieval of Image/Video Database, San Jose, CA, January 2004. [link]
  


9. SVM Classification
• Christopher J. C Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Journal of Data Mining and Knowledge Discovery, pp. 121-167, 1998. [link]
• O. Chapelle, P. Haffner, and V. N. Vapnik, "Support Vector Machines for Histogram-Based Image Classification," IEEE Trans. on Neural Networks, Vol. 10, No. 5, Sept. 1999. [link]
• Guodong Guo and Stan.Z. Li, "Content-based Audio Classification and Retrieval by Support Vector Machines," IEEE Trans. on Neural Networks. Vol.14, No.1, pp.209-215. January 2003. [link]
• J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. Feature selection for SVMs. In Sara A Solla, Todd K Leen, and Klaus-Robert Muller, editors, Advances in Neural Information Processing Systems 13. MIT Press, 2001. [link]
  


10. Relevance Feedback and Active Learning
• Rui, Y., Huang, T. S., Ortega, M., , Mehrotra, S.: Relevance feedback: A power tool in interactive content-based image retrieval. IEEE Trans. on Circuits and Systems for Video Technology 8(5) (Sep. 1998):644--655 [link]
• G. Salton and C. Buckley. Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, pages 288--297, 1990 [link]
• Tong, S., & Koller, D. (2000). Support vector machine active learning with applications to text classification, Proceedings of the Seventeenth International Conference on Machine Learning (pp. 999- 1006). San-Francisco: Morgan Kaufmann. [link]
• S. Tong and E. Chang, "Support Vector Machine Active Learning for Image Retrieval," ACM International Conference on Multimedia, pp.107-118, Ottawa, October 2001. [link]
  


11. HMM and Video Classification
• L.R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proceedings of the IEEE, Volume: 77, Issue: 2, Feb 1989, pp. 257-286. [link]
• Jeff Bilmes, What HMMs can do, U. Washington Tech Report, Feb 2002. [link]
• J. Huang, Z. Liu, and Y. Wang, "Joint Video Scene Segmentation and Classification based on Hidden Markov Model," Proc. IEEE International Conference on Multimedia and Expo (ICME 2000), New York, Aug. 2000. [link]
• L. Xie, S.-F. Chang, A. Divakaran and H. Sun, "Structure Analysis of Soccer Video with Hidden Markov Models," ICASSP-2002, Orlando, FL, USA, May 2002. [link]
  


12. Image/Video Segmentation and Editing
• J. Shi and J. Malik. Normalized cuts and image segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'97), pages 731--737, 1997. [link]
• Chad Carson, Serge Belongie, Hayit Greenspan, and Jitendra Malik , Blobworld: Image segmentation using Expectation-Maximization and its application to image querying, IEEE Trans. PAMI, 2002
• C. Rother, V. Kolmogorov and A. Blake. Interactive Foreground Extraction using Iterated Graph Cuts. ACM Transactions on Graphics (SIGGRAPH'04), 2004. [link]
• Li, Y., Sun, J., and Shum, H. 2005. Video object cut and paste. In ACM SIGGRAPH 2005 Papers (Los Angeles, California, July 31 - August 04, 2005). J. Marks, Ed. SIGGRAPH '05. ACM Press, New York, NY, 595-600
  
13. Multimodal Fusion
• Caruana, R., Niculescu-Mizil, A., Crew, G., and Ksikes, A. Ensemble selection from libraries of models. ICML '04, Banff, Alberta, Canada, July, 2004.
• Woods, K., Kegelmeyer, W. P., and Bowyer, K., Combination of Multiple Classifiers Using Local Accuracy Estimates. IEEE Trans. Pattern Anal. Mach. Intell. Apr. 1997, 405-410.
  
14. context, social image tagging
• Amit Zunjarwad, Hari Sundaram, and Lexing Xie, "Contextual Wisdom: Social Relations and Correlations for Multimedia Event Annotation," ACM Multimedia 2007. [link]
• Bageshree Shevade Hari Sundaram Lexing Xie ,"Modeling Personal and Social Network Context for Event Annotation in Images," JCDL 2007. [link]
• Lyndon Kennedy, Mor Naaman, Shane Ahern, Rahul Nair, Tye Rattenbury. How Flickr Helps us Make Sense of the World: Context and Content in Community-Contributed Media Collections.. In proceedings, Fifteenth ACM International Conference on Multimedia, (ACM MM 2007), September 2007, Augsburg, Germany.
• Tye Rattenbury, Nathan Good, Mor Naaman. Towards Automatic Extraction of Event and Place Semantics from Flickr Tags. In Proceedings, Thirtieth International ACM SIGIR Conference, (SIGIR 2007), July 2007, Amsterdam, The Netherlands.
• Shane Ahern, Mor Naaman, Rahul Nair, Jeannie Yang. World Explorer: Visualizing Aggregate Data from Unstructured Text in Geo-Referenced Collections. In proceedings, Seventh ACM/IEEE-CS Joint Conference on Digital Libraries, (JCDL 07), June 2007, Vancouver, Canada. Winner, Vannevar Bush Best Paper Award.
• Morgan Ames and Mor Naaman. Why We Tag: Motivations for Annotation in Mobile and Online Media. In Proceedings of the SIGCHI conference on Human Factors in computing systems (CHI 2007), San Jose, CA, USA, 2007. BibTex.
• Mor Naaman, Yee Jiun Song, Andreas Paepcke, Hector Garcia-Molina. Automatic Organization for Digital Photographs with Geographic Coordinates. In proceedings, Fourth ACM/IEEE-CS Joint Conference on Digital Libraries, (JCDL 04), June 2004. Winner, Vannevar Bush Best Paper Award. Talk slides (ppt)
  
15. Multimodal Image/Video Retrieval
    • Lyndon Kennedy, Paul Natsev, Shih-Fu Chang. Automatic Discovery of Query Class Dependent Models for Multimodal Search. In ACM Multimedia, Singapore, November 2005.
    • Alexander G. Hauptmann, Rong Yan, Wei-hao Lin, Michael Christel and Howard Wactlar
      Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
      In IEEE Transaction on Multimedia, Aug. 2007.
    • Apostol Natsev, Alexander Haubold, Jelena Tesic, Lexing Xie, Rong Yan
      Semantic Concept-Based Query Expansion and Re-ranking for Multimedia Retrieval
      In ACM Multimedia, 2007
16. Clustering and Topic Discovery
    • pLSA
    • LDA