Erik Sudderth Dissertation Abstracts

  • Adams, N. J., & Williams, C. K. I. (2003). Dynamic trees for image modelling. Image and Vision Computing, 21, 865–877. CrossRefGoogle Scholar

  • Amit, Y., & Trouvé, A. (2007). Generative models for labeling multi-object configurations in images. In J. Ponce, et al. (Ed.), Toward category-level object recognition. Berlin: Springer. Google Scholar

  • Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D. M., & Jordan, M. I. (2003). Matching words and pictures. Journal of Machine Learning Research, 3, 1107–1135. MATHCrossRefGoogle Scholar

  • Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4), 509–522. CrossRefGoogle Scholar

  • Bienenstock, E., Geman, S., & Potter, D. (1997). Compositionality, MDL priors, and object recognition. In Neural information processing systems 9 (pp. 838–844). Cambridge: MIT Press. Google Scholar

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. MATHCrossRefGoogle Scholar

  • Borenstein, E., & Ullman, S. (2002). Class-specific, top-down segmentation. In European conference on computer vision (Vol. 2, pp. 109–122). Google Scholar

  • Bosch, A., Zisserman, A., & Muñoz, X. (2006). Scene classification via pLSA. In European conference on computer vision (pp. 517–530). Google Scholar

  • Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698. CrossRefGoogle Scholar

  • Casella, G., & Robert, C. P. (1996). Rao–Blackwellisation of sampling schemes. Biometrika, 83(1), 81–94. MATHCrossRefMathSciNetGoogle Scholar

  • Csurka, G., et al. (2004). Visual categorization with bags of keypoints. In ECCV workshop on statistical learning in computer vision. Google Scholar

  • De Iorio, M., Müller, P., Rosner, G. L., & MacEachern, S. N. (2004). An ANOVA model for dependent random measures. Journal of the American Statistical Association, 99(465), 205–215. MATHCrossRefMathSciNetGoogle Scholar

  • DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics, 44, 837–845. MATHCrossRefGoogle Scholar

  • Escobar, M. D., & West, M. (1995). Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association, 90(430), 577–588. MATHCrossRefMathSciNetGoogle Scholar

  • Fei-Fei, L., & Perona, P. (2005). A Bayesian hierarchical model for learning natural scene categories. In IEEE conference on computer vision and pattern recognition (Vol. 2, pp. 524–531). Google Scholar

  • Fei-Fei, L., Fergus, R., & Perona, P. (2004). Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. In CVPR workshop on generative model based vision. Google Scholar

  • Fergus, R., Fei-Fei, L., Perona, P., & Zisserman, A. (2005). Learning object categories from Google’s image search. In International conference on computer vision (Vol. 2, pp. 1816–1823). Google Scholar

  • Fink, M., & Perona, P. (2004). Mutual boosting for contextual inference. In Neural information processing systems 16. Cambridge: MIT Press. Google Scholar

  • Fischler, M. A., & Elschlager, R. A. (1973). The representation and matching of pictorial structures. IEEE Transactions on Computers, 22(1), 67–92. CrossRefGoogle Scholar

  • Frey, B. J., & Jojic, N. (2003). Transformation-invariant clustering using the EM algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(1), 1–17. CrossRefGoogle Scholar

  • Gelfand, A. E., Kottas, A., & MacEachern, S. N. (2005). Bayesian nonparametric spatial modeling with Dirichlet process mixing. Journal of the American Statistical Association, 100(471), 1021–1035. MATHCrossRefMathSciNetGoogle Scholar

  • Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis. London: Chapman & Hall. MATHGoogle Scholar

  • Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101, 5228–5235. CrossRefGoogle Scholar

  • He, X., Zemel, R. S., & Carreira-Perpiñán, M. A. (2004). Multiscale conditional random fields for image labeling. In IEEE conference on computer vision and pattern recognition (Vol. 2, pp. 695–702). Google Scholar

  • Helmer, S., & Lowe, D. G. (2004). Object class recognition with many local features. In CVPR workshop on generative model based vision. Google Scholar

  • Hinton, G. E., Ghahramani, Z., & Teh, Y. W. (2000). Learning to parse images. In Neural information processing systems 12 (pp. 463–469). Cambridge: MIT Press. Google Scholar

  • Ishwaran, H., & James, L. F. (2001). Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association, 96(453), 161–173. MATHCrossRefMathSciNetGoogle Scholar

  • Ishwaran, H., & Zarepour, M. (2002). Dirichlet prior sieves in finite normal mixtures. Statistica Sinica, 12, 941–963. MATHMathSciNetGoogle Scholar

  • Jin, Y., & Geman, S. (2006). Context and hierarchy in a probabilistic image model. In IEEE conference on computer vision and pattern recognition (Vol. 2, pp. 2145–2152). Google Scholar

  • Jojic, N., & Frey, B. J. (2001). Learning flexible sprites in video layers. In IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 199–206). Google Scholar

  • Jordan, M. I. (2004). Graphical models. Statistical Science, 19(1), 140–155. MATHCrossRefMathSciNetGoogle Scholar

  • Jordan, M. I. (2005). Dirichlet processes, Chinese restaurant processes and all that. Tutorial at Neural Information Processing Systems. Google Scholar

  • Kovesi, P. (2005). MATLAB and Octave functions for computer vision and image processing. Available from http://www.csse.uwa.edu.au/~pk/research/matlabfns/.

  • LeCun, Y., Huang, F. J., & Bottou, L. (2004). Learning methods for generic object recognition with invariance to pose and lighting. In IEEE conference on computer vision and pattern recognition (Vol. 2, pp. 97–104). Google Scholar

  • Leibe, B., Leonardis, A., & Schiele, B. (2004). Combined object categorization and segmentation with an implicit shape model. In ECCV workshop on statistical learning in computer vision. Google Scholar

  • Liter, J. C., & Bülthoff, H. H. (1998). An introduction to object recognition. Zeitschrift für Naturforschung, 53c, 610–621. Google Scholar

  • Loeff, N., Arora, H., Sorokin, A., & Forsyth, D. (2006). Efficient unsupervised learning for localization and detection in object categories. In Neural information processing systems 18 (pp. 811–818). Cambridge: MIT Press. Google Scholar

  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. CrossRefGoogle Scholar

  • MacEachern, S. N. (1999). Dependent nonparametric processes. In Proceedings section on Bayesian statistical science (pp. 50–55). Alexandria: American Statistical Association. Google Scholar

  • Matas, J., Chum, O., Urban, M., & Pajdla, T. (2002). Robust wide baseline stereo from maximally stable extremal regions. In British machine vision conference (pp. 384–393). Google Scholar

  • Mikolajczyk, K., & Schmid, C. (2004). Scale and affine invariant interest point detectors. International Journal of Computer Vision, 60(1), 63–86. CrossRefGoogle Scholar

  • Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 1615–1630. CrossRefGoogle Scholar

  • Milch, B., Marthi, B., Russell, S., Sontag, D., Ong, D. L., & Kolobov, A. (2005). BLOG: Probabilistic models with unknown objects. In International joint conference on artificial intelligence 19 (pp. 1352–1359) Google Scholar

  • Miller, E. G., & Chefd’hotel, C. (2003). Practical nonparametric density estimation on a transformation group for vision. In IEEE conference on computer vision and pattern recognition (Vol. 2, pp. 114–121). Google Scholar

  • Miller, E. G., Matsakis, N. E., & Viola, P. A. (2000). Learning from one example through shared densities on transforms. In IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 464–471). Google Scholar

  • Murphy, K., Torralba, A., & Freeman, W. T. (2004). Using the forest to see the trees: A graphical model relating features, objects, and scenes. In Neural information processing systems 16. Cambridge: MIT Press. Google Scholar

  • Neal, R. M. (2000). Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 9(2), 249–265. CrossRefMathSciNetGoogle Scholar

  • Pitman, J. (2002). Combinatorial stochastic processes. Technical Report 621, U.C. Berkeley Department of Statistics, August 2002. Google Scholar

  • Rodriguez, A., Dunson, D. B., & Gelfand, A. E. (2006). The nested Dirichlet process. Working Paper 2006-19, Duke Institute of Statistics and Decision Sciences. Google Scholar

  • Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. (2004). The author-topic model for authors and documents. In Uncertainty in artificial intelligence 20 (pp. 487–494). Corvallis: AUAI Press. Google Scholar

  • Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2005). LabelMe: A database and web-based tool for image annotation. Technical Report 2005-025, MIT AI Lab. Google Scholar

  • Shepard, R. N. (1980). Multidimensional scaling, tree-fitting, and clustering. Science, 210, 390–398. CrossRefMathSciNetGoogle Scholar

  • Simard, P. Y., LeCun, Y. A., Denker, J. S., & Victorri, B. (1998). Transformation invariance in pattern recognition: Tangent distance and tangent propagation. In B. O. Genevieve & K. R. Müller (Eds.), Neural networks: tricks of the trade (pp. 239–274). Berlin: Springer. CrossRefGoogle Scholar

  • Siskind, J. M., Sherman, J., Pollak, I., Harper, M. P., & Bouman, C. A. (2004, submitted). Spatial random tree grammars for modeling hierarchal structure in images. IEEE Transactions on Pattern Analysis and Machine Intelligence. Google Scholar

  • Sivic, J., Russell, B. C., Efros, A. A., Zisserman, A., & Freeman, W. T. (2005). Discovering objects and their location in images. In International conference on computer vision (Vol. 1, pp. 370–377). Google Scholar

  • Storkey, A. J., & Williams, C. K. I. (2003). Image modeling with position-encoding dynamic trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(7), 859–871. CrossRefGoogle Scholar

  • Sudderth, E. B. (2006). Graphical models for visual object recognition and tracking. PhD thesis, Massachusetts Institute of Technology. Google Scholar

  • Sudderth, E. B., Torralba, A., Freeman, W. T., & Willsky, A. S. (2005). Learning hierarchical models of scenes, objects, and parts. In International conference on computer vision (Vol. 2, pp. 1331–1338). Google Scholar

  • Sudderth, E. B., Torralba, A., Freeman, W. T., & Willsky, A. S. (2006a). Depth from familiar objects: A hierarchical model for 3D scenes. In IEEE conference on computer vision and pattern recognition (Vol. 2, pp. 2410–2417). Google Scholar

  • Sudderth, E. B., Torralba, A., Freeman, W. T., & Willsky, A. S. (2006b). Describing visual scenes using transformed Dirichlet processes. In Neural information processing systems 18 (pp. 1297–1304). Cambridge: MIT Press. Google Scholar

  • Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006). Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101(476), 1566–1581. MATHCrossRefMathSciNetGoogle Scholar

  • Tenenbaum, J. M., & Barrow, H. G. (1977). Experiments in interpretation-guided segmentation. Artificial Intelligence, 8, 241–274. CrossRefGoogle Scholar

  • Torralba, A. (2003). Contextual priming for object detection. International Journal of Computer Vision, 53(2), 169–191. CrossRefGoogle Scholar

  • Torralba, A., Murphy, K. P., & Freeman, W. T. (2004). Sharing features: Efficient boosting procedures for multiclass object detection. In IEEE conference on computer vision and pattern recognition (Vol. 2, pp. 762–769). Google Scholar

  • Tu, Z., Chen, X., Yuille, A. L., & Zhu, S. C. (2005). Image parsing: Unifying segmentation, detection, and recognition. International Journal of Computer Vision, 63(2), 113–140. CrossRefGoogle Scholar

  • Ullman, S., Vidal-Naquet, M., & Sali, E. (2002). Visual features of intermediate complexity and their use in classification. Nature Neuroscience, 5(7), 682–687. Google Scholar

  • Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154. CrossRefGoogle Scholar

  • Weber, M., Welling, M., & Perona, P. (2000). Unsupervised learning of models for recognition. In European conference on computer vision (pp. 18–32). Google Scholar

  • Williams, C. K. I., & Allan, M. (2006). On a connection between object localization with a generative template of features and pose-space prediction methods. Informatics Research Report 719, University of Edinburgh. Google Scholar

  • Random discrete measures include models such as the Dirichlet process and the Pitman-Yor process. In applications, these models are typically used as priors on the mixing measure of a mixture model (e.g. Dirichlet process mixtures).

    Dirichlet and Pitman-Yor processes

    A concise introduction to the Dirichlet process is:


    • [PDF]
    Perhaps the best way to get to grips with Dirichlet process mixtures is to understand the inference algorithms. There is one and only one article to read on the basic Gibbs samplers:


    • [MathSciNet]
    A more detailed introduction to the Dirichlet process and its technical properties is the following book chapter:


    • [MathSciNet]
    A key reference on Dirichlet processes and stick-breaking is a classic article by Ishwaran and James, which first made ideas such as stick-breaking constructions and Pitman-Yor processes accessible to machine learning researchers. The name "Pitman-Yor process" also seems to appear here for the first time.


    • [MathSciNet]
    The Pitman-Yor process was introduced by Perman, Pitman and Yor. Their article is still the authoritative reference.


    • [MathSciNet]
    For a non-technical introduction to the Pitman-Yor process, have a look at Yee Whye Teh's article on Kneser-Ney smoothing, which applies the Pitman-Yor process to an illustrative problem in language processing.


    • [PDF]

    Generalizations

    Dirichlet processes and Pitman-Yor processes are two examples of random discrete probabilities. Any random discrete probability measure can in principle be used to replace the Dirichlet process in mixture models or one of its other applications (infinite HMMs etc). Over the past few years, it has become much clearer which models exist, how they can be represented, and in which cases we can expect inference to be tractable. If you are interested in understanding how these models work and what the landscape of nonparametric Bayesian clustering models looks like, I recommend the following two articles:


    • [MathSciNet] [PDF]


    • [PDF]
    This talk by Igor Prünster gives a clear and concise overview:


    • [Videolectures]
    Random discrete measures are usually obtained using stick-breaking constructions and related techniques. The construction of models which do not admit such representations is a bit more demanding. For the construction of general random measures, see


    • [PDF] [MathSciNet]

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