Understanding image representations by measuring their equivariance and equivalence

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

Volumn 07-12-June-2015, Issue , 2015, Pages 991-999

  Lenc, Karel ^a   Vedaldi, Andrea ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; INVARIANCE; NEURAL NETWORKS;

CONVOLUTIONAL NEURAL NETWORK; EQUIVARIANCE; GEOMETRIC INVARIANCE; HISTOGRAMS OF ORIENTED GRADIENTS; IMAGE REPRESENTATIONS; MATHEMATICAL PROPERTIES; NUMBER OF METHODS; VISUAL INFORMATION;

PATTERN RECOGNITION;

EID: 84959210421     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298701     Document Type: Conference Paper

References (35)

1. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. In Proc. BMVC, 2014
2. X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. Yuille. Detect what you can: Detecting and representing objects using holistic models and body parts. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014
3. G. Csurka, C. R. Dance, L. Dan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In Proc. ECCV Workshop on Stat. Learn. in Comp. Vision, 2004
4. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proc. CVPR, 2005
5. M. Denil,, B. Shakibi, L. Dinh, M. Ranzato, and N. de Freitas. Predicting parameters in deep learning. In Proc. NIPS, 2013
6. 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. CoRR, abs/1310. 1531, 2013
7. M. Everingham, A. Zisserman, C. Williams, and L. V. Gool. The PASCAL visual obiect classes challenge 2007 (VOC2007) results. Technical report, Pascal Challenge, 2007
8. I. Goodfellow, H. Lee, Q. V. Le, A. Saxe, and A. Y. Ng. Measuring invariances in deep networks. In Advances in neural information processing systems, pages 646-654, 2009
9. H. Jégou, M. Douze, C. Schmid, and P. Pérez. Aggregating local descriptors into a compact image representation. In Proc. CVPR, 2010
10. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Proc. NIPS, 2012
11. T. Leung and J. Malik. Representing and recognizing the visual appearance of materials using three-dimensional textons. IJCV, 43(1), 2001
12. T. Lindeberg. Principles for automatic scale selection. Technical Report ISRN KTH/NA/P 98/14 SE, Royal Institute of Technology, 1998
13. D. G. Lowe. Object recognition from local scale-invariant features. In Proc. ICCV, 1999
14. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2(60):91-110, 2004
15. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. In Proc. CVPR, 2003
16. O. Parkhi, A. Vedaldi, C. V. Jawahar, and A. Zisserman. The truth about cats and dogs. In Proc. ICCV, 2011
17. F. Perronnin and C. Dance. Fisher kernels on visual vocabularies for image categorizaton. In Proc. CVPR, 2006
18. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. CNN features off-the-shelf: an astounding baseline for recognition. In CVPR DeepVision Workshop, 2014
19. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. Imagenet large scale visual recognition challenge, 2014
20. U. Schimdt and S. Roth. Learning rotation-aware features: From invariant priors to equivariant descriptors. In Proc. CVPR, 2012
21. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Le-Cun. Overfeat: Integrated recognition, localization and detection using convolutional networks. volume abs/1312. 6229, 2014
22. L. Sifre and S. Mallat. Rotation, scaling and deformation invariant scattering for texture discrimination. In Proc. CVPR, 2013
23. K. Simonyan, A. Vedaldi, and A. Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. In ICLR Workshop, 2013
24. J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In Proc. ICCV, 2003
25. K. Sjöstrand, L. H. Clemmensen, R. Larsen, and B. Ersbøll. Spasm: A matlab toolbox for sparse statistical modeling. Journal of Statistical Software, 2012
26. K. Sohn and H. Lee. Learning invariant representations with local transformations. CoRR, abs/1206. 6418, 2012
27. B. Taskar, C. Guestrin, and D. Koller. Max-margin markov networks. In Proc. NIPS, 2003
28. A. Vedaldi and B. Fulkerson. VLFeat-An open and portable library of computer vision algorithms. In Proc. ACM Int. Conf. on Multimedia, 2010
29. A. Vedaldi and K. Lenc. MatConvNet-convolutional neural networks for MATLAB. CoRR, abs/1412. 4564, 2014
30. C. Vondrick, A. Khosla, T. Malisiewicz, and A. Torralba. HOGgles: Visualizing object detection features. In Proc. ICCV, 2013
31. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Localityconstrained linear coding for image classification. Proc. CVPR, 2010
32. J. Yang, K. Yu, and T. Huang. Supervised translation-invariant sparse coding. In Proc. CVPR, 2010
33. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. CoRR, abs/1311. 2901, 2013
34. B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning Deep Features for Scene Recognition using Places Database. NIPS, 2014
35. X. Zhou, K. Yu, T. Zhang, and T. S. Huang. Image classification using super-vector coding of local image descriptors. In Proc. ECCV, 2010.