Learning tree-structured descriptor quantizers for image categorization

BMVC 2011 - Proceedings of the British Machine Vision Conference 2011

Volumn , Issue , 2011, Pages

  Krapac, Josip ^a   Verbeek, Jakob ^a   Jurie, Frédéric ^b  

^a INRIA   (France)

^b UNIVERSITÉ DE CAEN   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DECISION TREES; FORESTRY; IMAGING SYSTEMS; INFORMATION RETRIEVAL; OPTIMIZATION; TREES (MATHEMATICS);

BAG OF WORDS; CLASSIFICATION PERFORMANCE; COMPUTATIONALLY EFFICIENT; IMAGE APPEARANCE; IMAGE CATEGORIZATION; MODEL IMAGES; QUANTIZATION INDEX; TREE-STRUCTURED;

IMAGE CLASSIFICATION;

EID: 84898403829     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C25.47     Document Type: Conference Paper

References (33)

1. Y-L. Boureau, F. Bach, Y. Le Cun, and J. Ponce. Learning mid-level features for recognition. In CVPR, 2010.
2. L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
3. G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In ECCV International Workshop on Statistical Learning in Computer Vision, 2004.
4. B. Fulkerson, A. Vedaldi, and S. Soatto. Localizing objects with smart dictionaries. In ECCV, 2008.
5. P. Geurts, D. Ernst, and L. Wehenkel. Extremely randomized trees. Machine Learning, 63(1):3-42, 2006.
6. T. Joachims. A support vector method for multivariate performance measures. In ICML, 2005.
7. F. Jurie and B. Triggs. Creating efficient codebooks for visual recognition. In ICCV, pages 604-610, 2005.
8. J. Kelley. The cutting-plane method for solving convex programs. SIAM Journal on Control and Optimization, 1960.
9. J. Krapac, J. Verbeek, and F. Jurie. Modeling spatial layout with Fisher vectors for image categorization. In ICCV, 2011.
10. S. Lazebnik and M. Raginsky. Supervised learning of quantizer codebooks by information loss minimization. Pattern Analysis and Machine Intelligence, 31(7):1294-1309, July 2009.
11. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
12. B. Leibe, K. Mikolajczyk, and B. Schiele. Efficient clustering and matching for object class recognition. In BMVC, 2006.
13. T. Leung and J. Malik. Representing and recognizing the visual appearance of materials using three-dimensional textons. IJCV, 43(1):29-44, 2001.
14. X. Lian, Z. Li, B. Lu, and L. Zhang. Max-margin dictionary learning for multiclass image categorization. In ECCV, 2010.
15. D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
16. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Supervised dictionary learning. In NIPS, 2008.
17. S. Maji and A. Berg. Max-margin additive models for detection. In ICCV, 2009.
18. F. Moosmann, E. Nowak, and F. Jurie. Randomized clustering forests for image classification. Pattern Analysis and Machine Intelligence, 30(9):1632-1646, 2008.
19. David Nistér and Henrik Stewénius. Scalable recognition with a vocabulary tree. In CVPR, 2006.
20. E. Nowak, F. Jurie, and B. Triggs. Sampling strategies for bag-of-features image classification. In ECCV, 2006.
21. A. Opelt and A. Pinz. Object localization with boosting and weak supervision for generic object recognition. In SCIA, 2005.
22. F. Perronnin. Universal and adapted vocabularies for generic visual categorization. Pattern Analysis and Machine Intelligence, 30(7):1243-1256, July 2008.
23. F. Perronnin, J. Sánchez, and Y. Liu. Large-scale image categorization with explicit data embedding. In CVPR, 2010.
24. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In CVPR, 2008.
25. M. Roizman and M. Last. Look-ahead mechanism integration in decision tree induction models. In Advances in Web Intelligence and Data Mining, 2006.
26. J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In ICCV, 2003.
27. J. van Gemert, C. Veenman, A. Smeulders, and J.-M. Geusebroek. Visual word ambiguity. Pattern Analysis and Machine Intelligence, 32(7):1271-1283, 2010.
28. M. Varma and D. Ray. Learning the discriminative power-invariance trade-off. In ICCV, 2007.
29. J. Winn, A. Criminisi, and T. Minka. Object categorization by learned universal visual dictionary. In ICCV, 2005.
30. J. Yang, K. Yu, and T. Huang. Supervised translation-invariant sparse coding. In CVPR, 2010.
31. L. Yang, R. Jin, R. Sukthankar, and F. Jurie. Unifying discriminative visual codebook generation with classifier training for object category recognition. In CVPR, 2008.
32. J. Zhang, M. Marszałek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision, 73(2):213-238, 2007.
33. W. Zhang, A. Surve, X. Fern, and T. Dietterich. Learning non-redundant codebooks for classifying complex objects. In ICML, 2009.