Unsupervised object discovery: A comparison

International Journal of Computer Vision

Volumn 88, Issue 2, 2010, Pages 284-302

  Tuytelaars, Tinne ^a   Lampert, Christoph H ^b   Blaschko, Matthew B ^b,c   Buntine, Wray ^d,e  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^c UNIVERSITY OF OXFORD   (United Kingdom)

^d CSIRO   (Australia)

^e AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

Evaluation; Object discovery; Unsupervised object recognition

Indexed keywords

BASELINE METHODS; DATA SETS; EVALUATION; LATENT VARIABLE MODELS; OBJECT CLASS; SPECTRAL CLUSTERING METHODS; UNLABELED DATA;

OBJECT RECOGNITION;

EID: 77951296653     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-009-0271-8     Document Type: Article

References (32)

1. Bart, E., Porteous, I., & Perona, P. (2008). Unsupervised learning of visual taxonomies. In Proceedings of the IEEE conference on computer vision and pattern recognition.
2. M. Belkin P. Niyogi 2003 Laplacian eigenmaps for dimensionality reduction and data representation Neural Computation 15 6 1373 1396 1085.68119 10.1162/089976603321780317 (Pubitemid 37049796)
3. Y. Bengio O. Delalleau N. Le Roux J.-F. Paiement P. Vincent M. Ouimet 2004 Learning eigenfunctions links spectral embedding and kernel pca Neural Computation 16 10 2197 2219 1085.68120 10.1162/0899766041732396 (Pubitemid 39137820)
4. I. Biederman 1987 Recognition-by-components: A theory of human image understanding Psychological Review 94 2 115 147 10.1037/0033-295X.94.2.115
5. D. M. Blei A. Y. Ng M. I. Jordan 2003 Latent Dirichlet allocation Journal of Machine Learning Research 3 993 1022 1112.68379 10.1162/jmlr.2003.3.4-5.993
6. Buntine, W. L. (2002). Variational extensions to EM and multinomial PCA. In 13th European conference on machine learning (ECML'02), Helsinki, Finland.
7. Buntine, W. L., & Jakulin, A. (2006). Discrete components analysis. In C. Saunders, M. Grobelnik, S. Gunn & J. Shawe-Taylor (Eds.), Subspace, latent structure and feature selection techniques. Berlin: Springer.
8. Canny, J. (2004). GaP: a factor model for discrete data. In SIGIR 2004 (pp. 122-129).
9. Chapelle, O., Haffner, P., & Vapnik, V. (1999). Svms for histogram-based image classification. In IEEE transactions on neural networks, special issue on support vectors.
10. B. S. Clarke A. R. Barron 1994 Jeffrey's prior is asymptotically least favorable under entropy risk Journal of Statistical Planning and Inference 41 37 60 0820.62006 10.1016/0378-3758(94)90153-8 1292146
11. Grauman, K., & Darrell, T. (2006). Unsupervised learning of categories from sets of partially matching image features. In Proceedings of the IEEE conference on computer vision and pattern recognition.
12. Griffin, G., Holub, A., & Perona, P. (2007). Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology.
13. Hofmann, T. (1999). Probabilistic latent semantic indexing. In Research and development in information retrieval (pp. 50-57).
14. Kim, G., Faloutsos, C., & Hebert, M. (2008). Unsupervised modeling of object categories using link analysis techniques. In IEEE conference on computer vision and pattern recognition.
15. D. Lee H. Seung 1999 Learning the parts of objects by non-negative matrix factorization Nature 401 788 791 10.1038/44565 (Pubitemid 29512513)
16. Liu, D., & Chen, T. (2007). A topic-motion model for unsupervised video object discovery. In Proceedings of the IEEE conference on computer vision and pattern recognition.
17. D. Lowe 2004 Distinctive image features from scale-invariant keypoint International Journal of Computer Vision 2 60 91 110 10.1023/B:VISI.0000029664. 99615.94
18. M. Meila 2007 Comparing clusterings: an information based distance Journal of Multivariate Analysis 98 873 895 05170498 10.1016/j.jmva.2006.11.013 2325412
19. K. Mikolajczyk C. Schmid 2004 Scale and affine invariant interest point detectors International Journal of Computer Vision 1 60 63 86 10.1023/B:VISI.0000027790.02288.f2
20. Ng, A., Jordan, M., & Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems, Vol. 14. Cambridge: MIT Press.
21. Philbin, J., Chum, O., Isard, M., Sivic, J., & Zisserman, A. (2007). Object retrieval with large vocabularies and fast spatial matching. In Proceedings of the IEEE conference on computer vision and pattern recognition.
22. Rosenberg, A., & Hirschberg, J. (2007). V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning, EMNLP-CoNLL (pp. 410-420).
23. Russell, B. C., Efros, A. A., Sivic, J., Freeman, W. T., & Zisserman, A. (2006). Using multiple segmentations to discover objects and their extent in image collections. In Proceedings of the IEEE conference on computer vision and pattern recognition.
24. B. Schölkopf A. Smola K.-R. Müller 1998 Nonlinear component analysis as a kernel eigenvalue problem Neural Computation 10 5 1299 1319 10.1162/089976698300017467
25. Shotton, J., Winn, J., Rother, C., & Criminisi, A. (2006). Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In Proceedings European conference on computer vision.
26. Sivic, J., Russell, B. C., Efros, A., Zisserman, A., & Freeman, W. T. (2005). Discovering object categories in image collections. In Proceedings of the international conference on computer vision.
27. Sivic, J., Russell, B. C., Zisserman, A., Freeman, W. T., & Efros, A. A. (2008). Unsupervised discovery of visual object class hierarchies. In Proceedings of the IEEE conference on computer vision and pattern recognition.
28. Tang, J., & Lewis, P. (2008). Non-negative matrix factorisation for object class discovery and image auto-annotation. In ACM international conference on image and video retrieval.
29. Todorovic, S., & Ahuja, N. (2006). Extracting subimages of an unknown category from a set of images. In Proceedings of the IEEE conference on computer vision and pattern recognition.
30. U. von Luxburg 2007 A tutorial on spectral clustering Statistics and Computing 17 4 395 416 10.1007/s11222-007-9033-z 2409803
31. Wang, X., & Grimson, E. (2008). Spatial latent Dirichlet allocation. In Proceedings of neural information processing systems conference.
32. Weber, M., Welling, M., & Perona, P., (2000). Towards automatic discovery of object categories. In Proceedings of the IEEE conference on computer vision and pattern recognition.