One-shot learning of object categories

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 28, Issue 4, 2006, Pages 594-611

  Fei Fei, Li ^a   Fergus, Rob ^b   Perona, Pietro ^c  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Few images; Learning; Object categories; Priors; Recognition; Unsupervised; Variational inference

Indexed keywords

DATABASE SYSTEMS; IMAGE PROCESSING; LEARNING ALGORITHMS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; PROBABILITY DENSITY FUNCTION; VARIATIONAL TECHNIQUES;

MAXIMUM A POSTERIORI (MAP) METHODS; OBJECT CATEGORIES; POSTERIOR MODEL; VARIATIONAL INFERENCE;

OBJECT RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BIOLOGICAL MODEL; CLUSTER ANALYSIS; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CLUSTER ANALYSIS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; INFORMATION STORAGE AND RETRIEVAL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 33144466753     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2006.79     Document Type: Article

References (43)

1. Merriam-Webster's Collegiate Dictionary, 10th ed., Springfield, Mass.: Merriam-Webster, Inc., 1994.
2. Y. Amit and D. Geman, "A Computational Model for Visual Selection," Neural Computation, vol. 11, no. 7, pp. 1691-1715, 1999.
3. H. Attias, "Inferring Parameters and Structure of Latent Variable Models by Variational Bayes," Proc. 15th Conf. Uncertainty in Artificial Intelligence, pp. 21-30, 1999.
4. I. Biederman, "Recognition-by-Components: A Theory of Human Image Understanding," Psychological Rev., vol. 94, pp. 115-147, 1987.
5. M. Burl and P. Perona, "Recognition of Planar Object Classes," Proc. Conf. Computer Vision and Pattern Recognition, pp. 223-230, 1996.
6. M. Burl, M. Weber, and P. Perona, "A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry," Proc. European Conf. Computer Vision, pp. 628-641, 1996.
7. A. Dempster, N. Laird, and D. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," J. Royal Statistical Soc., vol. 29, pp. 1-38, 1976.
8. L. Fei-Fei, R. Fergus, and P. Perona, "A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories," Proc. Ninth Int'l Conf. Computer Vision, pp. 1134-1141, Oct. 2003.
9. L. Fei-Fei, R. Fergus, and P. Perona, "Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories," Proc. Workshop Generative-Model Based Vision, 2004.
10. L. Fei-Fei, R. Fergus, and P. Perona, supplemental material, http://computer.org/tpami/archives.htm, 2006.
11. P. Felzenszwalb and D. Huttenlocher, "Pictorial Structures for Object Recognition," Int'l J. Computer Vision, vol. 1, pp. 55-79, 2005.
12. P. Felzenszwalb and D. Huttenlocher, "Representation and Detection of Deformable Shapes," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 208-220, Feb. 2005.
13. R. Fergus, P. Perona, and A. Zisserman, "Object Class Recognition by Unsupervised Scale-Invariant Learning," Proc. Computer Vision and Pattern Recognition, pp. 264-271, 2003.
14. R. Fergus, P. Perona, and A. Zisserman, "A Visual Category Filter for Google Images," Proc. Eighth European Conf. Computer Vision, 2004.
15. R. Fergus, P. Perona, and A. Zisserman, "A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition," Proc. Computer Vision and Pattern Recognition, 2005.
16. D. Forsyth and A. Zisserman, "Shape from Shading in the Light of Mutual Illumination," Image and Vision Computing, pp. 42-29, 1990.
17. A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin, Bayesian Data Analysis. Chapman Hall/CRC, 1995.
18. R. Gilks, S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice. Chapman Hall, 1992.
19. R. Gilks and P. Wild, "Adaptive Rejection Sampling for Gibbs Sampling," Applied Statistics, vol. 41, pp. 337-348, 1992.
20. W. Grimson and D. Huttenlocher, "On the Sensitivity of the Hough Transformfor Object Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 3, pp. 255-274, Mar. 1990.
21. K. Humphreys and M. Titterington, "Some Examples of Recursive Variational Approximations for Bayesian Inference," Advanced Mean Field Methods. M. Opper and D. Saad, eds., MIT Press, 2001.
22. D. Huttenlocher, G. Klanderman, and W. Rucklidge, "Comparing Images Using the Hausdorff Distance," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850-863, Sept. 1993.
23. T. Kadir and M. Brady, "Scale, Saliency and Image Description," Int'l J. Computer Vision, vol. 45, no. 2, pp. 83-105, 2001.
24. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
25. Y. LeCun, F. Huang, and L. Bottou, "Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting," Proc. Conf. Computer Vision and Pattern Recognition, 2004.
26. T. Leung, M. Burl, and P. Perona, "Finding Faces in Cluttered Scenes Using Labeled Random Graph Matching," Proc. Int'l Conf. Computer Vision, pp. 637-644, 1995.
27. D. Lowe, "Object Recognition from Local Scale-Invariant Features," Proc. Int'l Conf. Computer Vision, pp. 1150-1157, 1999.
28. K. Mikolajczyk and C. Schmid, "An Affine Invariant Interest Point Detector," Proc. European Conf. Computer Vision, vol. 1, pp. 128-142, 2002.
29. R. Neal and G. Hinton, "A View of the EM Algorithm that Justifies Incremental, Sparse and Other Variants," Learning in Graphical Models, M.I. Jordan, ed., pp. 355-368, Norwell, Mass.: Kluwer Academic Press, 1998.
30. W. Penny, "Variational Bayes for d-Dimensional Gaussian Mixture Models," technical report, Univ. College London, 2001.
31. F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce, "3D Object Modeling and Recognition Using Affine-Invariant Patches and Multi-View Spatial Constraints," Proc. Computer Vision and Pattern Recognition, pp. 272-280, 2003.
32. H. Rowley, S. Baluja, and T. Kanade, "Neural Network-Based Face Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-38, Jan. 1998.
33. E. Sali and S. Ullman, "Combining Class-Specific Fragments for Object Classification," Proc. British Machine Vision Conf., vol. 1, pp. 203-213, 1999.
34. H. Schneiderman and T. Kanade, "A Statistical Approach to 3D Object Detection Applied to Faces and Cars," Proc. Computer Vision and Pattern Recognition, pp. 746-751, 2000.
35. K. Sung and T. Poggio, "Example-Based Learning for View-Based Human Face Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 39-51, Jan. 1998.
36. P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proc. Computer Vision and Pattern Recognition, vol. 1, pp. 511-518, 2001.
37. P. Viola, M. Jones, and D. Snow, "Detecting Pedestrians Using Patterns of Motion and Appearance," Proc. Int'l Conf. Computer Vision, pp. 734-741, 2003.
38. M. Weber, W. Einhaeuser, M. Welling, and P. Perona, "Viewpoint-Invariant Learning and Detection of Human Heads," Proc. Fourth Int'l Conf. Automated Face and Gesture Recognition, pp. 20-27, 2000.
39. M. Weber, M. Welling, and P. Perona, "Unsupervised Learning of Models for Recognition," Proc. European Conf. Computer Vision, vol. 2, pp. 101-108, 2000.
40. A. Torralba, K.P. Murphy, and W.T. Freeman, Sharing Features: Efficient Boosting Procedures for Multiclass Object Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 762-769, 2004.
41. M. Weber, "Unsupervised Learning of Models for Object Recognition," PhD thesis, Calif. Inst. of Technology, Pasadena, 2000.
42. R. Fergus, "Visual Object Category Recognition," PhD thesis, Univ. of Oxford, U.K., 2005.
43. A. Berg, T. Berg, and J. Malik, "Shape Matching and Object Recognition Using Low Distortion Correspondence," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 26-33, June 2005.