Learning discriminative hierarchical features for object recognition

IEEE Signal Processing Letters

Volumn 21, Issue 9, 2014, Pages 1159-1163

  Zuo, Zhen ^a   Wang, Gang ^a,b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b ADVANCED DIGITAL SCIENCES CENTER   (Singapore)

Author keywords

Discriminant analysis; hierarchical feature learning; object recognition; patch to class distance

Indexed keywords

DISCRIMINANT ANALYSIS; IMAGE CLASSIFICATION; LEARNING SYSTEMS;

CLASSIFICATION ACCURACY; DIFFERENT CLASS; FEATURE LEARNING; FEATURE SPACE; HIERARCHICAL FEATURES; LOCAL FEATURE; NON-LINEAR TRANSFORMATIONS; PATCH-TO-CLASS DISTANCE;

OBJECT RECOGNITION;

EID: 84902133494     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2014.2298888     Document Type: Article

References (18)

1. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
2. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in CVPR, 2005, vol. 1, pp. 886-893, IEEE.
3. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507, 2006.
4. Q. V. Le,W. Y. Zou, S. Y. Yeung, and A. Y. Ng, "Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis," in CVPR, 2011, pp. 3361-3368, IEEE.
5. O. Boiman, E. Shechtman, and M. Irani, "In defense of nearestneighbor based image classification," in CVPR, 2008, pp. 1-8, IEEE.
6. W. Y. Zou, S. Y. Zhu, A. Y. Ng, and K. Yu, "Deep learning of invariant features via simulated fixations in video," in NIPS, 2012, pp. 3212-3220.
7. Q. V. Le, A. Karpenko, J. Ngiam, and A. Y. Ng, "Ica with reconstruction cost for efficient overcomplete feature learning," in NIPS, 2011, pp. 1017-1025.
8. H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations," in ICML, 2009, pp. 609-616, ACM.
9. A. Hyvärinen, J. Hurri, and P. Hoyer, Natural Image Statistics: A Probabilistic Approach to Early Computational Vision. Berlin, Germany: Springer, 2009.
10. M. Muja and D. G. Lowe, "Fast approximate nearest neighbors with automatic algorithm configuration," in VISSAPP, 2009, pp. 331-340.
11. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in CVPR, 2006, vol. 2, pp. 2169-2178, IEEE.
12. 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," Comput. Vis. Image Understand., vol. 106, no. 1, pp. 59-70, 2007.
13. A. Coates, H. Lee, and A. Y. Ng, "An analysis of single-layer networks in unsupervised feature learning," in Int. Conf. Artificial Intelligence and Statistics, 2011, pp. 215-223.
14. S. McCann and D. G. Lowe, "Local naive bayes nearest neighbor for image classification," in CVPR, 2012, pp. 3650-3656, IEEE.
15. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, "Localityconstrained linear coding for image classification," in CVPR, 2010, pp. 3360-3367, IEEE.
16. K. Yu, Y. Lin, and J. Lafferty, "Learning image representations from the pixel level via hierarchical sparse coding," in CVPR, 2011, pp. 1713-1720, IEEE.
17. K. Sohn, D. Y. Jung, H. Lee, and A. O. Hero, "Efficient learning of sparse, distributed, convolutional feature representations for object recognition," in ICCV, 2011, pp. 2643-2650, IEEE.
18. R. Gens and P. Domingos, "Discriminative learning of sum-product networks," in NIPS, 2012, pp. 3248-3256.