A Bayesian approach to unsupervised one-shot learning of object categories

A Bayesian network formulation for relational shape matching is presented. The main advantage of the relational shape matching approach is the obviation of the nonrigid spatial mappings used by recent nonrigid matching approaches. The basic variables that need to be estimated in the relational shape matching objective function are the global rotation and scale and the local displacements and correspondences. The new Bethe free energy approach is used to estimate the pairwise correspondences between links of the template graphs and the data. The resulting framework is useful in both registration and recognition contexts. Results are shown on hand-drawn templates and on 2D transverse T1-weighted MR images.

[1]  M. Fiedler.  A property of eigenvectors of nonnegative symmetric matrices and its application to graph theory , 1975 .

[2]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[3]  I. Biederman.  Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[4]  William Grimson,et al.  Object recognition by computer - the role of geometric constraints , 1991 .

[5]  H. C. Longuet-Higgins,et al.  An algorithm for associating the features of two images , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[6]  Michael Brady,et al.  Feature-based correspondence: an eigenvector approach , 1992, Image Vis. Comput..

[7]  Eric Mjolsness,et al.  New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence , 1998, NIPS.

[8]  Hemant D. Tagare,et al.  A geometric criterion for shape-based non-rigid correspondence , 1995, Proceedings of IEEE International Conference on Computer Vision.

[9]  Alex Pentland,et al.  Modal Matching for Correspondence and Recognition , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1995, Proceedings of IEEE International Conference on Computer Vision.

[11]  Steve R. Waterhouse,et al.  Bayesian Methods for Mixtures of Experts , 1995, NIPS.

[12]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[13]  Paul M. Thompson,et al.  A surface-based technique for warping three-dimensional images of the brain , 1996, IEEE Trans. Medical Imaging.

[14]  Yali Amit,et al.  Graphical Templates for Model Registration , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Pietro Perona,et al.  A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry , 1998, ECCV.

[17]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[19]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[20]  Yali Amit,et al.  A Computational Model for Visual Selection , 1999, Neural Computation.

[21]  Hagai Attias,et al.  Inferring Parameters and Structure of Latent Variable Models by Variational Bayes , 1999, UAI.

[22]  Kaleem Siddiqi,et al.  Matching Hierarchical Structures Using Association Graphs , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Pietro Perona,et al.  Unsupervised Learning of Models for Recognition , 2000, ECCV.

[25]  Yair Weiss,et al.  Correctness of Local Probability Propagation in Graphical Models with Loops , 2000, Neural Computation.

[26]  Takeo Kanade,et al.  A statistical approach to 3d object detection applied to faces and cars , 2000 .

[27]  Ulrich Eckhardt,et al.  Shape descriptors for non-rigid shapes with a single closed contour , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[28]  M. Hagedoorn.  Pattern matching using similarity measures , 2000 .

[29]  Anand Rangarajan,et al.  Self-annealing and self-annihilation: unifying deterministic annealing and relaxation labeling , 2000, Pattern Recognit..

[30]  W. Freeman,et al.  Bethe free energy, Kikuchi approximations, and belief propagation algorithms , 2001 .

[31]  Thomas P. Minka,et al.  Using lower bounds to approxi-mate integrals , 2001 .

[32]  Alan L. Yuille,et al.  MIME: Mutual Information Minimization and Entropy Maximization for Bayesian Belief Propagation , 2001, NIPS.

[33]  Eric Mjolsness,et al.  A relationship between spline-based deformable models and weighted graphs in non-rigid matching , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[34]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[35]  Philip N. Klein,et al.  Shock-Based Indexing into Large Shape Databases , 2002, ECCV.

[36]  Alan L. Yuille,et al.  CCCP Algorithms to Minimize the Bethe and Kikuchi Free Energies: Convergent Alternatives to Belief Propagation , 2002, Neural Computation.

[37]  James M. Coughlan,et al.  Finding Deformable Shapes Using Loopy Belief Propagation , 2002, ECCV.

[38]  Nanning Zheng,et al.  Stereo Matching Using Belief Propagation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Nanning Zheng,et al.  Stereo Matching Using Belief Propagation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[41]  Anand Rangarajan,et al.  A new point matching algorithm for non-rigid registration , 2003, Comput. Vis. Image Underst..

[42]  Michael Brady,et al.  Saliency, Scale and Image Description , 2001, International Journal of Computer Vision.

[43]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[44]  Norman I. Badler,et al.  Multi-Level Shape Representation Using Global Deformations and Locally Adaptive Finite Elements , 1997, International Journal of Computer Vision.