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Joint Visual Text Modeling
Detecting, Recognizing and Finding Faces
Statistical Machine Translation
Machine Learning
Information Retrieval


Joint Visual Text Modeling

K. Barnard and D. A. Forsyth. Exploiting image semantics for picture libraries. In The First ACM/IEEE-CS Joint Conference on Digital Libraries, page 469, 2001.
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K. Barnard and D. A. Forsyth. Learning the semantics of words and pictures. In Int. Conf. on Computer Vision, pages 408-415, 2001.
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K. Barnard, P. Duygulu, and D. A. Forsyth. Clustering art. In IEEE Conf. on Computer Vision and Pattern Recognition, volume 2, pages 434-441, 2001.
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P. Duygulu, K. Barnard, N.d. Freitas, and D. A. Forsyth. Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In Seventh European Conference on Computer Vision (ECCV), volume 4, pages 97-112, Copenhagen, Denmark, May 27 - June 2 2002.
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K. Barnard, P. Duygulu, N. de Freitas, D. A. Forsyth, D. Blei, and M. Jordan. Matching words and pictures. Journal of Machine Learning Research, 3:1107-1135, 2003.
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K. Barnard, P. Duygulu, R. Guru, P. Gabbur, and D. A. Forsyth. The effects of segmentation and feature choice in a translation model of object recognition. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Madison, Wisconsin, June 16-22 2003.
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P. Duygulu-Sahin. Translating Images to words: A novel approach for object recognition. PhD thesis, Middle East Technical University, Turkey, 2003.
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J.Edwards, R. White, and D. Forsyth. Words and pictures in the news. In HLT-NAACL03 Workshop on Learning Word Meaning from Non-Linguistic Data, Edmonton, Canada, May 31 2003.
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K. Barnard, M. Johnson, and D. Forsyth. Word sense disambiguation with pictures. In HLT-NAACL03 Workshop on Learning Word Meaning from Non-Linguistic Data, Edmonton, Canada, May 31 2003.
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P. Carbonetto and N. de Freitas. Why can't jose read? the problem of learning semantic associations in a robot environment. In HLT-NAACL03 Workshop on Learning Word Meaning from Non-Linguistic Data, Edmonton, Canada, May 31 2003.
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P. Carbonetto, N. de Freitas, and K. Barnard. A statistical model for general contextual object recognition. In Eight European Conference on Computer Vision (ECCV), 2004.
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Detecting, Recognizing and Finding Faces

H. Schneiderman and T.Kanade. Object detection using statistics of parts. International Journal of Computer Vision, 2002.
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S.L. Phung, A. Bouzerdoum, and D. Chai. Skin segmentation using color pixel classification: analysis and comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 27(1), January 2005.
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W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld. Face recognition: A literature survey. ACM Computing Surveys, 35(4):399-458, 2003.
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B. S. Manjunath, R. Chellappa, and C. von der Malsburg. A feature based approach to face recognition. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 373-378, June, 1992.
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T. Miller, A. C. Berg, J. Edwards, M. Maire, R. White, Y.-W. Teh, E. Learned-Miller, and D.A. Forsyth. Faces and names in the news. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2004.
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T. Miller, A. C. Berg, J. Edwards, and D.A. Forsyth. Who is in the picture. In Neural Information Processing Systems (NIPS), 2004.
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S. Satoh and T. Kanade. Name-it: Association of face and name in video. In Proc. of CVPR'97, pages 368-373, 1997.
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X. Song, C.-Y. Lin, and M.-T. Sun. Cross-modality automatic face model training from large video databases. In 1st IEEE Workshop on Face Processing in video, in conjunction with CVPR 2004, Washington D.C., USA, June 28, 2004.
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J. Yang, M-Y. Chen and A. Hauptmann. Finding person x: Correlating names with visual appearances. In International Conference on Image and Video Retrieval (CIVR'04), Dublin City University, Ireland, July 21-23 2004.
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M-Y. Chen and A. Hauptmann. Searching for a specific person in broadcast news video. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP'04), Montreal, Canada, May 17-21 2004.
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Statistical Machine Translation

P. F. Brown, J. Cocke, V. Della Pietra, S. Della Pietra, F. Jelinek, J. D. Lafferty, R. L. Mercer, and P. S. Roossin. A statistical approach to machine translation. Computational Linguistics, 6(2):79-85, 1990.
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P.F. Brown, S. A. Della Pietra, V. J. Della Pietra, and R. L. Mercer. The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2):263-311, 1993.
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A. Yaser, J. Curin, M. Jahr, K. Knight, J. Lafferty, I.D. Melamed, F. J. Och, D. Purdy, N. A. Smith, and D. Yarowsky. Statistical machine translation: Final report. Technical report, Johns Hopkins University 1999 Summer Workshop on Language Engineering, Center for Language and Speech Processing, Baltimore, MD, USA, 1999.
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K. Knight. Statistical mt tutorial workbook.
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F. J. Och and H. Ney. Statistical machine translation. In EAMT Workshop, pages 39-46, Ljubljana, Slovenia, May 2000.
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S. Vogel, F. J. Och, C. Tillmann, S. Nießen, H. Sawaf, and H. Ney. Statistical methods for machine translation. In Wolfgang Wahlster, editor, Verbmobil: Foundations of Speech-to-Speech Translation, pages 377-393, Berlin, July 2000. Springer Verlag.
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I. D. Melamed. Empirical Methods for Exploiting Parallel Texts. MIT Press, 2001.
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I. D. Melamed. Models of translational equivalence among words. Computational Linguistics, 26(2):221-249, June 2000.
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I. D. Melamed. Models of co-occurrence. Technical Report TR-98-05, IRCS Technical Report, 1998.
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Machine Learning

A. P. Dempster, N. M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, 1(39):1-38, 1977.
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J. Bilmes. A gentle tutorial on the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical Report ICSI-TR-97-021, University of California at Berkeley, 1997.
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C. Andrieu, N. de Freitas, A. Doucet, and M. I. Jordan. An introduction to mcmc for machine learning. Machine Learning, 50(1-2), January-February 2003.
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G. Celeux, D. Chauveau, and J. Duebolt. On stochastic versions of the em algorithm. Technical Report RR-2514, INRIA, March 1995.
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R.A. Levine and G. Casella. Implementations of the monte carlo em algorithm. Journal of Computational and Graphical Statistics, (10):422-439, 2001.
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F. Dellaert. The expectation maximization algorithm. Technical Report GIT-GVU-02-20, College of Computing, Georgia Institute of Technology, February 2002.
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F. Dellaert, S. Seitz, C. Thorpe, and S. Thrun. Feature correspondence: A markov chain monte carlo approach. In Neural Information Processing Systems (NIPS), 2000.
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T.M. Mitchell. Machine learning and data mining. Communications of the ACM, 42(11), November 1999.
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A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the 1998 Conference on Computational Learning Theory, July 1998.
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D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
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D. M. Blei, T. L. Griffiths, M. Jordan, and J.B.Tenenbaum. Hierarchical topic models and the nested chinese restaurant process. In Neural Information Processing Systems (NIPS), 2003.
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Y. Ivanov, B. Blumberg, and Alex Pentland. Expectation-maximization for weakly labeled data. In 18th International Conference on Machine Learning, Williamstown, MA, June 2001.
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K. Nigam, A. K. McCallum, S. Thrun, and T. M. Mitchell. Learning to classify text from labeled and unlabeled documents. In Proceedings of AAAI-98, 15th Conference of the American Association for Artificial Intelligence, pages 792-799, Madison, US, 1998.
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K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using em. Machine Learning, 39(2/3):103-134, 2000.
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T. Hofmann and J. Puzicha. Statistical models for co-occurrence data. Technical Report 1635, Massachusetts Institute of Technology, 1998.
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T. Hofmann. Learning and representing topic. a hierarchical mixture model for word occurrence in document databases. In Proceedings of the Conference for Automated Learning and Discovery (CONALD), Pittsburgh, 1998.
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T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning Journal, 42(1):177-196, 2001.
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N. Tishby, F. Pereira, and W. Bialek. The information bottleneck method. In The 37th annual Allerton Conference on Communication, Control, and Computing, 1999.
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N. Friedman, O. Mosenzon, N. Slonim, and N. Tishby. Multivariate information bottleneck. In UAI-2001.
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C. H. Papadimitriou, P. Raghavan, H. Tamaki, and S. Vempala. Latent semantic indexing: A probabilistic analysis. In PODS98.
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A. Corduneanu and T. Jaakkola. Continuation methods for mixing heterogeneous sources. In Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence, 2002.
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Information Retrieval

A. Berger and J. Lafferty. Information retrieval as statistical translation. In Proceedings of the 1999 ACM SIGIR Conference on Research and Development in Information Retrieval, pages 222-229, 1999.
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J. Ponte and W. B. Croft. A language modeling approach to information retrieval. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, pages 275-281, 1998.
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V. Lavrenko and W. B. Croft. Relevance-based language models. In 24th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'01), 2001.
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V. Lavrenko and M. Choquette W.B. Croft. Cross-lingual relevance models. In 25th annual international ACM SIGIR conference on Research and Development in Information Retrieval, Tampere, Finland, August 11 - 15 2002.
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D. H. Miller, T. Leek, and R. Schwartz. A hidden markov model information retrieval system. In Proceedings of the 1999 ACM SIGIR Conference on Research and Development in Information Retrieval, pages 214-221, 1999.
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R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley and Sons, Inc., New York, 2000.
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D. A. Forsyth and J. Ponce. Computer Vision: a modern approach. Prentice-Hall, 2002.
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  • Automatic Image Annotation

  • [Mori-1999]
    Image-to-word transformation based on dividing and vector quantizing imageswith words
    Y. Mori, H. Takahashi, R. Oka (RWCP, Japan)
    MISRM'99 First International Workshop on Multimedia Intelligent Storage and Retrieval Management October 30th, 1999 Orlando, Florida, in conjonction with ACM Multimedia Conference 1999
  • [MaronRatan-1998]
    Multiple-Instance Learning for Natural Scene Classification,
    O. Maron and A. L. Ratan,
  • [Maron-thesis]
    Learning from Ambiguity,
    O. Maron,
    Ph.D. Thesis, MIT, May 1998.
  • [LiWang-PAMI2003]
    J. Li, J. Z. Wang,
    Automatic linguistic indexing of pictures by a statistical modeling approach,
    IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, 14 pp., 2003
  • [WangLi]
    J. Z. Wang and J. Li
    Mining Digital Imagery Data for Automatic Linguistic Indexing of Pictures
    Proc. NSF Workshop on Next Generation Data Mining
  • [Blei-SIGIR2003]
    Modeling annotated data
    D. M. Blei and M. I. Jordan
    26th International Conference on Research and Development in Information Retrieval (SIGIR), 2003
  • [Jeon-SIGIR03]
    Automatic Image Annotation and Retrieval using cross-media relevance models
    J. Jeon, V. Lavrenko and R. Manmatha
    SIGIR 2003
  • [Lavrenko-NIPS2003]
    A Model for Learning the Semantics of Pictures
    V. Lavrenko, R. Manmatha, J. Jeon
    NIPS 2003
  • [Wenyin-2001]
    Semi-Automatic Image Annotation
    L. Wenyin, S. Dumais, Y. Sun, H. Zhang, M. Czerwinski, B. Field
    INTERACT2001, 8th IFIP TC.13 Conference on Human-Computer Interaction,
    Tokyo, Japan July 9-13, 2001
  • [Monay-MM2003]
    On Image Auto-Annotation with Latent Space Models
    F. Monay and D. Gatica-Perez
    Proc. ACM Int. Conf. on Multimedia (ACM MM)
    Berkeley, November 2003

    Retrieval and Browsing

  • [Carson2002] Blobworld: Color and Texture based Image Segmentation using EM and Its Application to Image Querying and Classification
    Chad Carson, Serge Belongie, Hayit Greenspan, Jitendra Malik
    PAMI, 24(8):1026-1038, August 2002.
  • [Salesin]
    Fast Multiresolution Image Querying
    Charles Jacobs, Adam Finkelstein, David Salesin
  • [Rubner98]
    Yossi Rubner, Carlo Tomasi, and Leonidas J. Guibas.
    A Metric for Distributions with Applications to Image Databases.
    Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India, January 1998, pages 59-66
  • [Goodrum00]
    A. A. Goodrum.
    Image information retrieval: An overview of current research.
    Informing Science, 3(2):63-- 66, February 2000.
  • [Rui98]
    Yong Rui, Thomas Huang, Shih-Fu Chang
    Image Retrieval : Current Techniques, Promising Directions and Open Issues
    Journal of VisualCommunication and Image Representation, Vol. 10. 39-62, March 1999
  • [Cascia98]
    M. La Cascia, S. Sethi, and S. Sclaroff,
    Combining Textual and Visual Cues for Content-based Image Retrieval on the World Wide Web
    Proc. IEEE Workshop on Content-Based Access of Image and Video Libraries, June, 1998.
  • [Aksoy00]
    Selim Aksoy, Robert M. Haralick,
    Feature Normalization and Likelihood-based Similarity Measures for Image Retrieval ,
    Pattern Recognition Letters, Special Issue on Image and Video Retrieval, 2000.
  • [Campbell]
    N. W. Campbell, W. P. J. Mackeown, B. T Thomas, and T. Troscianko.
    Interpreting Image Databases by Region Classification.
    Pattern Recognition (Special Edition on Image Databases), 30(4):555--563, April 1997
  • Digital Libraries and user studies

  • [Markkula]
    Marjo Markkula, Marius Tico, Bemmu Sepponen, Katja Nirkkonen, Eero Sormunen:
    A Test Collection for the Evaluation of Content-Based Image Retrieval Algorithms- A User and Task-Based Approach.
    Information Retrieval, 4, 275-293 , 2001
  • [Klavans-JCDL2003]
    Peter T. Davis, David K. Elson, Judith L. Klavans,
    Methods for Precise Named Entity Matching in Digital Collections,
    Third ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2003


  • [WordNet]
    Five Papers on WordNet


  • [Amit96]
    Y. Amit and A. Kong,
    Graphical templates for model registration,
    IEEE PAMI (1996).
  • [Belongie2002]
    Serge Belongie, Jitendra Malik and Jan Puzicha
    Shape Matching and Object Recognition Using Shape Contexts
    to appear, PAMI 24(3), March 2002.
  • [Sclaroff_ICCV2001]
    Region Segmentation via Deformable Model-Guided Split and Merge ,
    L. Liu, and and S. Sclaroff,
    International Conference on Computer Vision (ICCV) , Jul., 2001.
  • [Sclaroff_CBAIVL2000]
    Index Trees for Efficient Deformable Shape-based Retrieval,,
    L. Liu, S. Sclaroff,
    IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL) , Jun., 2000.
  • [Sclaroff_PAMI2001]
    Deformable Shape Detection and Description via Model-Based Region Grouping,
    S. Sclaroff, and L. Liu,
    IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 23(5), May, 2001.


  • [MDS] A Brief Introduction to Multidimensional Scaling and Its Applications

    Texture Synthesis

  • [Efros_99]
    A. A. Efros and T. K. Leung, Texture Synthesis by Non-parametric Sampling,
    ICCV 99
  • [Efros_2001]
    A. A. Efros and W. T. Freeman, Image Quilting for Texture Synthesis and Transfer,

    Information Theory

  • [Shannon]
    C. E. Shannon,
    ``A mathematical theory of communication,''
    Bell System Technical Journal, vol. 27, pp. 379-423 and 623-656, July and October, 1948.


  • [Balakrishnama]
    S. Balakrishnama, A. Ganapathiraju
    Linear Discriminant Analysis, A Brief tutorial
    Institute for Signal and Information Processing, March 2, 1998