Transformation pursuit for image classification

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2014, Pages 3646-3653

  Paulin, Mattis ^a   Revaud, Jérôme ^a   Harchaoui, Zaid ^a   Perronnin, Florent ^b   Schmid, Cordelia ^a  

^a UNIV GRENOBLE ALPES   (France)

^b XRCE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTIONAL NEURAL NETWORKS; IMAGE CLASSIFICATION; ITERATIVE METHODS;

AUTOMATIC SELECTION; BASIC TRANSFORMATIONS; COMPLEX TRANSFORMATIONS; FISHER VECTORS; IMAGE TRANSFORMATIONS; ORIGINAL IMAGES; SIMPLE APPROACH; TRAINING TIME;

IMAGE ENHANCEMENT;

EID: 84911429110     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.466     Document Type: Conference Paper

References (34)

1. Y. S. Abu-Mostafa. Hints. Neural Computation, 7(4), 1995.
2. Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid. Good practice in large-scale learning for image classification. IEEE TPAMI, 2013.
3. Y. Bengio. Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2009.
4. C. Bishop. Training with noise is equivalent to Tikhonov regularization. In Neural computation, 1995.
5. C. M. Bishop. Neural Networks for Pattern Recognition. Oxford UP, 1995.
6. L. Bottou. Stochastic gradient tricks. In Neural Networks, Tricks of the Trade, Reloaded. Springer, 2012.
7. M. Chen, Z. Xu, K. Weinberger, and F. Sha. Marginalized de-noising autoencoders for domain adaptation. In ICML, 2012.
8. S. Clinchant, G. Csurka, F Perronnin, and J.-M. Renders. XRCE's participation to Imageval. ImageEval workshop at CVIR, 2007.
9. D. DeCoste and M. Burl. Distortion-invariant recognition via jittered queries. In CVPR, 2000.
10. D. Decoste and B. Schölkopf. Training invariant support vector machines. Machine Learning, 2002.
11. J. Donahue, Y Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. ICML, 2014.
12. E. Gavves, B. Fernando, C. G M. Snoek, A. W. M. Smeul-ders, and T. Tuytelaars. Fine-grained categorization by alignments. In ICCV, 2013.
13. H. Jegou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. IEEE TPAMI, 2011.
14. A. Krizhevsky, I. Sutskever, and G Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012.
15. Y LeCun, L. Bottou, Y Bengio, and P. Haffner. Gradient-based learning applied to document recognition. In Proc. of the IEEE, 1998.
16. G Loosli, S. Canu, and L. Bottou. Training invariant support vector machines using selective sampling. In Large Scale Kernel Machines. MIT Press, 2007.
17. D.G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2004.
18. L. Maaten, M. Chen, S. Tyree, and K. Q. Weinberger. Learning with marginalized corrupted features. In ICML, 2013.
19. S. Mallat. A wavelet tour of signal processing (3rd ed.). Academic Press, 2008.
20. J. Marín, D. Vázquez, D. Gerónimo, and A. López. Learning appearance in virtual scenarios for pedestrian detection. In CVPR, 2010.
21. P. Niyogi, F. Girosi, and T. Poggio. Incorporating prior information in machine learning by creating virtual examples. Proceedings of the IEEE, 1998.
22. L. Pishchulin, A. Jain, C. Wojek, M. Andriluka, T. Thormählen, and B. Schiele. Learning people detection models from few training samples. In CVPR, 2011.
23. J. A. Rodriguez and D. Larlus. Predicting an object location using a global image representation. In ICCV, 2013.
24. J. Sánchez and F. Perronnin. High-dimensional signature compression for large-scale image classification. In CVPR, 2011.
25. J. Sánchez, F. Perronnin, and T. de Campos. Modeling the spatial layout of images beyond spatial pyramids. Pattern Recognition Letters, 2012.
26. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, 2002.
27. P. Simard, D. Steinkraus, and J. C. Platt. Best practices for convolutional neural networks applied to visual document analysis. In ICDAR, 2003.
28. K. Sohn and H. Lee. Learning invariant representations with local transformations. ICML, 2012.
29. T. Tuytelaars and K. Mikolajczyk. Local invariant feature detectors: A survey. Foundations and Trends in Computer Graphics and Vision, 3(3), 2007.
30. V. N. Vapnik. Statistical learning theory. Wiley, 1998.
31. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. In ICML, 2008.
32. C. Wah, S. Branson, P. Welinder, P. Perona, and S. Be-longie. The CUB-200-2011 Dataset. Technical report, Cal-Tech, 2011.
33. L. Wan, M. Zeiler, S. Zhang, Y. LeCun, and R. Fergus. Reg-ularization of neural networks using dropconnect. In ICML, 2013.
34. L. Yaeger, R. Lyon, and B. Webb. Effective training of a neural network character classifier for word recognition. In NIPS, 1996.