Texture synthesis using convolutional neural networks

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 262-270

  Gatys, Leon A ^a,b   Ecker, Alexander S ^a,b,c,d   Bethge, Matthias ^a,b,c  

^a UNIVERSITY OF TÜBINGEN   (Germany)

^b Bernstein Center for Computational Neuroscience   (Germany)

^c MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^d BAYLOR COLLEGE OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTION; INFORMATION SCIENCE; NETWORK LAYERS; OBJECT RECOGNITION;

CONVOLUTIONAL NEURAL NETWORK; NATURAL IMAGES; NATURAL TEXTURES; OBJECT INFORMATION; PERCEPTUAL QUALITY; STATISTICAL PROPERTIES; TEXTURE REPRESENTATION; TEXTURE SYNTHESIS;

NEURAL NETWORKS;

EID: 84965135705     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. B. Balas, L. Nakano, and R. Rosenholtz. A summary-statistic representation in peripheral vision explains visual crowding. Journal of vision, 9(12):13, 2009.
2. C. F. Cadieu, H. Hong, D. L. K. Yamins, N. Pinto, D. Ardila, E. A. Solomon, N. J. Majaj, and J. J. Di Carlo. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition. PLoS Comput Biol, 10(12):e1003963, December 2014.
3. M. Cimpoi, S. Maji, and A. Vedaldi. Deep convolutional filter banks for texture recognition and segmentation. arXiv:1411.6836 [cs], November 2014. arXiv: 1411.6836.
4. E. L. Denton, W. Zaremba, J. Bruna, Y. Le Cun, and R. Fergus. Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation. In NIPS, 2014.
5. A. Efros and T. K. Leung. Texture synthesis by non-parametric sampling. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, volume 2, pages 1033-1038. IEEE, 1999.
6. A. A. Efros and W. T. Freeman. Image quilting for texture synthesis and transfer. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 341-346. ACM, 2001.
7. J. Freeman and E. P. Simoncelli. Metamers of the ventral stream. Nature Neuroscience, 14(9):1195-1201, September 2011.
8. J. Freeman, C. M. Ziemba, D. J. Heeger, E. P. Simoncelli, and A. J. Movshon. A functional and perceptual signature of the second visual area in primates. Nature Neuroscience, 16(7):974-981, July 2013.
9. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. arXiv preprint arXiv:1406.4729, 2014.
10. D. J. Heeger and J. R. Bergen. Pyramid-based Texture Analysis/Synthesis. In Proceedings of the 22Nd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH'95, pages 229-238, New York, NY, USA, 1995. ACM.
11. M. Jaderberg, A. Vedaldi, and A. Zisserman. Speeding up Convolutional Neural Networks with Low Rank Expansions. In BMVC 2014, 2014.
12. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the ACM International Conference on Multimedia, pages 675-678. ACM, 2014.
13. B. Julesz. Visual Pattern Discrimination. IRE Transactions on Information Theory, 8(2), February 1962.
14. S. Khaligh-Razavi and N. Kriegeskorte. Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation. PLoS Comput Biol, 10(11):e1003915, November 2014.
15. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 27, pages 1097-1105, 2012.
16. V. Kwatra, A. Schödl, I. Essa, G. Turk, and A. Bobick. Graphcut textures: image and video synthesis using graph cuts. In ACM Transactions on Graphics (ToG), volume 22, pages 277-286. ACM, 2003.
17. V. Lebedev, Y. Ganin, M. Rakhuba, I. Oseledets, and V. Lempitsky. Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition. arXiv preprint arXiv:1412.6553, 2014.
18. Y. A. Le Cun, L. Bottou, G. B. Orr, and K. R. Müller. Efficient backprop. In Neural networks: Tricks of the trade, pages 9-48. Springer, 2012.
19. A. J. Movshon and E. P. Simoncelli. Representation of naturalistic image structure in the primate visual cortex. Cold Spring Harbor Symposia on Quantitative Biology: Cognition, 2015.
20. G. Okazawa, S. Tajima, and H. Komatsu. Image statistics underlying natural texture selectivity of neurons in macaque V4. PNAS, 112(4):E351-E360, January 2015.
21. J. Portilla and E. P. Simoncelli. A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients. International Journal of Computer Vision, 40(1):49-70, October 2000.
22. R. Rosenholtz, J. Huang, A. Raj, B. J. Balas, and L. Ilie. A summary statistic representation in peripheral vision explains visual search. Journal of vision, 12(4):14, 2012.
23. 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. arXiv:1409.0575 [cs], September 2014. arXiv: 1409.0575.
24. E. P. Simoncelli and W. T. Freeman. The steerable pyramid: A flexible architecture for multi-scale derivative computation. In Image Processing, International Conference on, volume 3, pages 3444-3444. IEEE Computer Society, 1995.
25. K. Simonyan and A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs], September 2014. arXiv: 1409.1556.
26. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going Deeper with Convolutions. arXiv:1409.4842 [cs], September 2014. arXiv: 1409.4842.
27. L. Wei, S. Lefebvre, V. Kwatra, and G. Turk. State of the art in example-based texture synthesis. In Eurographics 2009, State of the Art Report, EG-STAR, pages 93-117. Eurographics Association, 2009.
28. L. Wei and M. Levoy. Fast texture synthesis using tree-structured vector quantization. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pages 479-488. ACM Press/Addison-Wesley Publishing Co., 2000.
29. D. L. K. Yamins, H. Hong, C. F. Cadieu, E. A. Solomon, D. Seibert, and J. J. Di Carlo. Performanceoptimized hierarchical models predict neural responses in higher visual cortex. PNAS, page 201403112, May 2014.
30. C. Zhu, R. H. Byrd, P. Lu, and J. Nocedal. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software (TOMS), 23(4):550-560, 1997.