Nonnegative correlation coding for image classification

Science China Information Sciences

Volumn 59, Issue 1, 2016, Pages 1-14

  Dong, Zhen ^a   Liang, Wei ^a   Wu, Yuwei ^a   Pei, Mingtao ^a   Jia, Yunde ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

correlation coding; image classification; locality; nonnegativity; similarity

Indexed keywords

CODES (SYMBOLS); IMAGE CODING;

CORRELATION BETWEEN FEATURES; GRADIENT PROJECTION ALGORITHMS; IMAGE REPRESENTATIONS; LOCALITY; NON-NEGATIVITY; NUMERICAL SOLVERS; QUADRATIC CONVERGENCE; SIMILARITY;

IMAGE CLASSIFICATION;

EID: 84953838027     PISSN: 1674733X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11432-015-5289-7     Document Type: Article

References (35)

1. Sivic J, Zisserman A. Video google: A text retrieval approach to object matching in videos. In: Proceedings of the 9th Computer Vision Conference. Nice: IEEE, 2003. 1470–1477
2. Huang Y Z, Wu Z F, Wang L, et al. Feature coding in image classification: a comprehensive study. IEEE Trans Pattern Anal Mach Intel, 2013, 36: 493–506
3. Yang J, Yu K, Gong Y, et al. Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of the 22th Computer Vision and Pattern Recognition Conference. Miami: IEEE, 2009. 1794–1801
4. Zhang C, Liu J, Tian Q, et al. Image classification by non-negative sparse coding, low-rank and sparse decomposition. In: Proceedings of the 24th Computer Vision and Pattern Recognition Conference. Colorado Springs: IEEE, 2011. 1673–1680
5. Yu K, Zhang T, Gong Y, et al. Nonlinear learning using local coordinate coding. In: Proceedings of the 24th Advances in Neural Information Processing Systems. Vancouver: NIPS, 2009. 2223–2231
6. Gao S, Tsang I, Chia L, et al. Laplacian sparse coding, hypergraph laplacian sparse coding, and applications. IEEE Trans Pattern Anal Mach Intel, 2013, 35: 92–104
7. Zheng M, Bu J, Chen C, et al. Graph regularized sparse coding for image representation. IEEE Trans Image Process, 2011, 20: 1327–1336
8. Liu L Q, Wang L, Liu X W, et al. In defense of soft-assignment coding. In: Proceedings of the 13th Computer Vision Conference. Barcelona: IEEE, 2011. 2486–2493
9. Wang J, Yang J, Yu K, et al. Locality-constrained linear coding for image classification. In: Proceedings of the 23th Computer Vision and Pattern Recognition Conference. San Francisco: IEEE, 2010. 3360–3367
10. Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the 19th Computer Vision and Pattern Recognition Conference. San Francisco: IEEE, 2006. 2169–2178
11. Nesterov Y, Nesterov I U E. Introductory Lectures on Convex Optimization: A Basic Course. Berlin: Springer, 2004
12. van Gemert J C, Veenman C J, Smeulders A W, et al. Visual word ambiguity. IEEE Trans Pattern Anal Mach Intel, 2010, 32: 1271–1283
13. Liu Y, Wu F, Zhang Z, et al. Sparse representation using nonnegative curds and whey. In: Proceedings of the 23th Computer Vision and Pattern Recognition Conference. San Francisco: IEEE, 2010. 3578–3585
14. Gao S, Tsang I W, Chia L- T, et al. Local features are not lonely–Laplacian sparse coding for image classification. In: Proceedings of the 23th Computer Vision and Pattern Recognition Conference. San Francisco: IEEE, 2010. 3555–3561
15. Chung F R. Spectral Graph Theory. Washington DC: American Mathematical Society, 1997
16. Shabou A, Le Borgne H. Locality-constrained and spatially regularized coding for scene categorization. In: Proceedings of the 25th Computer Vision and Pattern Recognition Conference. Providence: IEEE, 2012. 3618–3625
17. Wang Z L, Feng J S, Yan S C, et al. Linear distance coding for image classification. IEEE Trans Image Process, 2013, 22: 537–548
18. Zhang T, Ghanem B, Liu S, et al. Low-rank sparse coding for image classification. In: Proceedings of the 14th Computer Vision Conference. Sydney: IEEE, 2013. 281–288
19. Zhang L, Ma C. Low-rank decomposition and Laplacian group sparse coding for image classification. Neurocomputing, 2014, 135: 339–347
20. Hoyer P O. Modeling receptive fields with non-negative sparse coding. Neurocomputing, 2003, 52: 547–552
21. Duchi J, Shalev-Shwartz S, Singer Y, et al. Efficient projections onto the l1-ball for learning in high dimensions. In: Proceedings of the 25th Machine Learning Conference. Helsinki: ACM, 2008. 272–279
22. Nesterov Y. A method of solving a convex programming problem with convergence rate O(1/k2). Soviet Math Dok, 1983, 27: 372–376
23. Li L J, Li F F. What, where and who? Classifying events by scene and object recognition. In: Proceedings of the 11th Computer Vision Conference. Rio de Janeiro: IEEE, 2007. 1–8
24. Li F F, Fergus R, Perona P, et al. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput Vis Image Underst, 2007, 106: 59–70
25. Griffin G, Holub A, Perona P, et al. Caltech-256 Object Category Dataset. Technical Report. Pasadena: California Institute of Technology, 2007
26. Everingham M, van Gool L, Williams C K I, et al. The pascal visual object classes (voc) challenge. Int J Comput Vis, 2010, 88: 303–338
27. Lowe D G. Distinctive image features from scale-invariant keypoints. Int J Comput Vis, 2004, 60: 91–110
28. Wu J, Rehg J M. Beyond the euclidean distance: creating effective visual codebooks using the histogram intersection kernel. In: Proceedings of the 12th Computer Vision Conference. Kyoto: IEEE, 2009. 630–637
29. Bao C, Wu Y, Ling H, et al. Real time robust l1 tracker using accelerated proximal gradient approach. In: Proceedings of the 25th Computer Vision and Pattern Recognition Conference. Providence: IEEE, 2012. 1830–1837
30. Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intel, 2009, 31: 210–227
31. Zhang Q, Li B. Discriminative k-svd for dictionary learning in face recognition. In: Proceedings of the 23th Computer Vision and Pattern Recognition Conference. San Francisco: IEEE, 2010. 2691–2698
32. Zhang C J, Liu J, Liang C, et al. Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition. Comput Vis Image Underst, 2014, 123: 14–22
33. Perronnin F, Dance C. Fisher kernels on visual vocabularies for image categorization. In: Proceedings of the 20th Computer Vision and Pattern Recognition Conference. Minneapolis: IEEE, 2007. 1–8
34. Zhou X, Yu K, Zhang T, et al. Image classification using super-vector coding of local image descriptors. In: Proceedings of the 11th European Conference on Computer Vision. Berlin: Springer, 2010. 6315. 141–154
35. Chatfield K, Lempitsky V, Vedaldi A, et al. The devil is in the details: an evaluation of recent feature encoding methods. In: Proceedings of the 22nd British Machine Vision Conference. Dundee: BMVA Press, 2011. 1–12