Learning compositional categorization models

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3953 LNCS, Issue , 2006, Pages 316-329

  Ommer, Björn ^a   Buhmann, Joachim M ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONSTRAINT THEORY; DATABASE SYSTEMS; IMAGE SEGMENTATION; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBABILITY;

IMAGE CATEGORIZATION; LOCALIZED FEATURE HISTOGRAMS; MULTI-SCALE FEATURE SET; SHARABLE COMPOSITIONS;

OBJECT RECOGNITION;

EID: 33745861370     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11744078_25     Document Type: Conference Paper

References (26)

1. Fischler, M.A., Elschlager, R.A.: The representation and matching of pictorial structures. IEEE Trans. Comput. 22 (1973)
2. Lades, M., Vorbrüggen, J.C., Buhmann, J.M., Lange, J., von der Malsburg, C., Würtz, R.P., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 42 (1993)
3. Leibe, B., Schiele, B.: Scale-invariant object categorization using a scale-adaptive mean-shift search. In: Pattern Recognition, DAGM. (2004)
4. Fergus, R., Perona, P., Zisserman, A.: A visual category filter for google images. In: ECCV. (2004)
5. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In: CVPR Workshop GMBV. (2004)
6. Agarwal, S., Awan, A., Roth, D.: Learning to detect objects in images via a sparse, part-based representation. IEEE Trans. Pattern Anal. Machine Intell. 26 (2004)
7. Ommer, B., Buhmann, J.M.: Object categorization by compositional graphical models. In: EMMCVPR. (2005)
8. Berg, A.C., Berg, T.L., Malik, J.: Shape matching and object recognition using low distortion correspondence. In: CVPR. (2005)
9. Geman, S., Potter, D.F., Chi, Z.: Composition Systems. Technical report, Division of Applied Mathematics, Brown University, Providence, RI (1998)
10. Bicderman, I.; Recognition-by-components: A theory of human image understanding. Psychological Review 94 (1987)
11. Ommer, B., Buhmann, J.M.: A compositionality architecture for perceptual feature grouping. In: EMMCVPR. (2003)
12. Lowe, D.G.: Perceptual Organization and Visual Recognition. Kluwer Academic Publishers, Norwell, MA (1985)
13. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Computer Vision 60 (2004)
14. Veltkamp, R.C., Tanase, M.: Content-based image and video retrieval. In: A Survey of Content-Based Image Retrieval Systems. Kluwer (2002)
15. Dance, C., Willamowski, J., Fan, L., Bray, C., Csurka, G.: Visual categorization with bags of keypoints. In: ECCV Workshop on Stat. Learn. in Comp. Vis. (2004)
16. Weber, M., Welling, M., Perona, P.: Unsupervised learning of models for recognition. In; ECCV. (2000)
17. Holub, A.D., Welling, M., Perona, P.: Combining generative models and fisher kernels for object class recognition, In: ICCV. (2005)
18. Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. J. Computer Vision 61 (2005)
19. Heisele, B., Serre, T., Pontil, M., Vetter, T., Poggio, T.: Categorization by learning and combining object parts. In: NIPS. (2001)
20. Borenstein, E., Sharon, E., Ullman, S.: Combining top-down and bottom-up segmentation. In: CVPR Workshop on Perceptual Organization in Comp. Vis. (2004)
21. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR. (2001)
22. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. Int. J. Computer Vision 60 (2004)
23. Winkler, G.: Image Analysis, Random Fields and Markov Chain Monte Carlo Methods - A Mathematical Introduction, 2nd edn, Springer (2003)
24. Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing visual features for multiclass and multiview object detection. In: CVPR. (2004)
25. Roth, V., Tsuda, K.: Pairwise coupling for machine recognition of hand-printed Japanese characters, In: CVPR. (2001)
26. Hastie, T., Tibshirani, R.: Classification by pairwise coupling. In: NIPS. (1998)