Large-scale category structure aware image categorization

Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Volumn , Issue , 2011, Pages

  Zhao, Bin ^a   Fei Fei, Li ^b   Xing, Eric P ^a  

^a Carnegie Mellon University   (United States)

^b Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGING SYSTEMS; LEARNING SYSTEMS; SEMANTICS;

DATA SET; HUMAN COGNITION; IMAGE CATEGORIZATION; LARGE-SCALE DATASETS; LARGE-SCALES; MEDIUM-SCALE; OBJECT CLASS; SEMANTIC RELATIONSHIPS; STRUCTURE-AWARE; VISUAL WORLD;

GRADIENT METHODS;

EID: 85162562805     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (38)

1. B. Bakker and T. Heskes. Task clustering and gating for bayesian multitask learning. JMLR, 4:83-99, 2003.
2. E. Bart and S. Ullman. Cross-generalization: learning novel classes from a single example by feature replacement. In CVPR, 2005.
3. A. Berg, J. Deng, and L. Fei-Fei. Large scale visual recognition challenge 2010. http://www.image-net.org/challenges/LSVRC/2010/, 2010.
4. A. Binder, K.-R. Mller, and M. Kawanabe.On taxonomies for multi-class image categorization. IJCV, pages 1-21, 2011.
5. C. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag New York, Inc., 2006.
6. O. Boiman, E. Shechtman, and M. Irani. In defense of nearest-neighbor based image classification. In CVPR, 2008.
7. A. Budanitsky and G. Hirst. Evaluating wordnet-based measures of lexical semantic relatedness. Comput. Linguist., 32:13-47, March 2006.
8. R. Caruana. Multitask learning. Machine Learning, 28:41-75, 1997.
9. X. Chen, Q. Lin, S. Kim, J. Carbonell, and E. P. Xing. Smoothing proximal gradient method for general structured sparse learning. In UAI, 2011.
10. C. Chu, S. Kim, Y. Lin, Y. Yu, G., A. Ng, and K.Olukotun. Map-reduce for machine learning on multicore. In NIPS. 2007.
11. J. Deng, A. Berg, K. Li, and L. Fei-Fei. What does classifying more than 10,000 image categories tell us? In ECCV, 2010.
12. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR, 2009.
13. J. Deng, S. Satheesh, A. Berg, and L. Fei-Fei. Fast and balanced: Efficient label tree learning for large scale object recognition. In NIPS, 2011.
14. 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. In CVPR Workshop on Generative-Model Based Vision, 2004.
15. L. Fei-Fei, R. Fergus, and P. Perona.One-shot learning of object categories. PAMI, 28:594-611, 2006.
16. R. Fergus, H. Bernal, Y.Weiss, and A. Torralba. Semantic label sharing for learning with many categories. In ECCV, ECCV'10, 2010.
17. T. Gao and D. Koller. Discriminative learning of relaxed hierarchy for large-scale visual recognition. In ICCV, 2011.
18. G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology, 2007.
19. L. Jacob, F. Bach, and J.-P. Vert. Clustered multi-task learning: A convex formulation. In NIPS, 2008.
20. R. Jenatton, J. Mairal, G.Obozinski, and F. Bach. Proximal methods for sparse hierarchical dictionary learning. In ICML, 2010.
21. S. Kim and E. Xing. Tree-guided group lasso for multi-task regression with structured sparsity. In ICML, 2010.
22. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
23. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proc. IEEE, 86:2278-2324, 1998.
24. Y. Lin, F. Lv, S. Zhu, M. Yang, T. Cour, K. Yu, L. Cao, and T. Huang. Large-scale image classification: fast feature extraction and svm training. In CVPR, 2011.
25. D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60:91-110, 2004.
26. E. Miller, N. Matsakis, and P. Viola. Learning from one example through shared densities on transforms. In CVPR, 2000.
27. 2 ). Doklady AN SSSR (translated as Soviet. Math. Docl.), 269:543-547, 1983.
28. A.Opelt, A. Pinz, and A. Zisserman. Incremental learning of object detectors using a visual shape alphabet. In CVPR, 2006.
29. F. Perronnin, J. Sanchez, and T. Mensink. Improving the fisher kernel for large-scale image classification. In ECCV, 2010.
30. A. Quattoni, M. Collins, and T. Darrell. Transfer learning for image classification with sparse prototype representations. In CVPR, 2008.
31. B. Russell, A. Torralba, K. Murphy, andW. Freeman. Labelme: A database and web-based tool for image annotation. IJCV, 77:157-173, 2008.
32. R. Salakhutdinov, A. Torralba, and Josh Tenenbaum. Learning to share visual appearance for multiclass object detection. In CVPR, 2011.
33. E. Sudderth, A. Torralba, W. Freeman, and A. Willsky. Learning hierarchical models of scenes, objects, and parts. In CVPR, 2005.
34. J. Tenenbaum and W. Freeman. Separating style and content with bilinear models. Neural Computation, 12:1247-1283, 2000.
35. R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight. Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society Series B, pages 91-108, 2005.
36. A. Torralba, R. Fergus, andW. Freeman. 80 million tiny images: A large data set for nonparametric object and scene recognition. PAMI, 30:1958-1970, 2008.
37. A. Torralba, K. Murphy, and W. Freeman. Sharing features: efficient boosting procedures for multiclass object detection. In CVPR, 2004.
38. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained linear coding for image classification. In CVPR, 2010.