Learning component-level sparse representation using histogram information for image classification

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 1519-1526

  Chiang, Chen Kuo ^a   Duan, Chih Hsueh ^a   Lai, Shang Hong ^a   Chang, Shih Fu ^b  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPACT REPRESENTATION; COMPONENT LEVELS; DATA SETS; DICTIONARY LEARNING; ENERGY MINIMIZATION; GROUP PROPERTIES; HISTOGRAM INFORMATION; IMPORTANCE MEASURE; SPARSE CODING; SPARSE REPRESENTATION; UNIFIED FRAMEWORK;

ALGORITHMS; GRAPHIC METHODS; IMAGE CODING;

INFORMATION USE;

EID: 84863062028     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126410     Document Type: Conference Paper

References (32)

1. http://www.robots.ox.ac.uk/vgg/research/caltech/phog.htm.
2. http://www.vlfeat.org/vedaldi/code/bag/bag.html.
3. M. Aharon, M. Elad, and A. Bruckstein. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. Signal Processing, IEEE Transactions on, 54(11):4311-4322, 2006.
4. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886-893 vol. 1, 2005.
5. B. Efron, T. Hastie, L. Johnstone, and R. Tibshirani. Least angle regression. Annals of Statistics, 32:407-499, 2004.
6. 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 Computer Vision and Pattern Recognition Workshop, 2004. CVPRW '04. Conference on, page 178, 2004.
7. P. Gehler and S. Nowozin. On feature combination for multiclass object classification. In Computer Vision, 2009 IEEE 12th International Conference on, 29 2009.
8. S. Hasler, H. Wersing, and E. Korner. Class-specific sparse coding for learning of object representations. In Artificial Neural Networks: Biological Inspirations V ICANN 2005. 2005.
9. S. Hasler, H. Wersing, and E. Körner. Combining reconstruction and discrimination with class-specific sparse coding. Neural Comput., 19:1897-1918, July 2007.
10. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, 2006.
11. H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In In NIPS, pages 801-808. NIPS, 2007.
12. Y.-Y. Lin, T.-L. Liu, and C.-S. Fuh. Local ensemble kernel learning for object category recognition. In CVPR, 2007.
13. J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online dictionary learning for sparse coding. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pages 689-696, New York, NY, USA, 2009. ACM.
14. J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res., 11:19-60, March 2010.
15. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Discriminative learned dictionaries for local image analysis. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8, 2008.
16. S. Mallat. A wavelet tour of signal processing.
17. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10):1615-1630, 2005.
18. M.-E. Nilsback and A. Zisserman. A visual vocabulary for flower classification. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, 2006.
19. M.-E. Nilsback and A. Zisserman. Automated flower classification over a large number of classes. In Computer Vision, Graphics Image Processing, 2008. ICVGIP '08. Sixth Indian Conference on, pages 722-729, 2008.
20. G. Obozinski, B. Taskar, and M. I. Jordan. Joint covariate selection and joint subspace selection for multiple classification problems. Statistics and Computing, 20:231-252, April 2010.
21. B. A. Olshausen and D. J. Fieldt. Sparse coding with an overcomplete basis set: A strategy employed by v1. Vision Research, 37:3311-3325, 1997.
22. G. Peyré. Sparse modeling of textures. J. Math. Imaging Vis., 34:17-31, May 2009.
23. I. Ramirez, P. Sprechmann, and G. Sapiro. Classification and clustering via dictionary learning with structured incoherence and shared features. In The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13-18 June 2010, pages 3501-3508. IEEE, 2010.
24. P. Sallee and B. A. Olshausen. Learning Sparse Multiscale Image Representations. NIPS, 15:1327-1334, 2003.
25. J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pages 1470-1477 vol.2, 2003.
26. K. Skretting and J. H. Husøy. Texture classification using sparse frame-based representations. EURASIP J. Appl. Signal Process., 2006:102-102, January 2006.
27. M. Varma and D. Ray. Learning the discriminative powerinvariance trade-off. In Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, October 2007.
28. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(2):210-227, 2009.
29. J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 1794-1801, 2009.
30. M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. Journal Of The Royal Statistical Society Series B, 68(1):49-67, 2006.
31. X.-T. Yuan and S. Yan. Visual classification with multi-task joint sparse representation. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 3493-3500, 2010.
32. J. Zhang. A probabilistic framework for multi-task learning. Technical Report CMU-LTI-06-006, Language Technologies Institute, CMU, 2006.