Abstract
Achieving structured low-rank representation from the original image is a challenging and significant task, owing to the capacity of the low-rank structure in expressing structured information from the real world. It is noteworthy that, most of the existing methods to obtain the low-rank textures, treat this issue as a “transformational problem”, which lead to the poor quality of the images with complex backgrounds. In order to jump out of this interference, we try to explore this issue as a “generative problem” and propose the Low-rank texture Generative Adversarial Network (LR-GAN) using an unsupervised image-to-image network. Our method generates the high-quality low-rank texture gradually from the low-rank constraint after many iterations of training. Considering that the low-rank constraint is difficult to optimize (NP-hard problem) in the loss function, we introduce the layer of the low-rank gradient filter to approach the optimal low-rank solution. Experimental results demonstrate that the proposed method is effective on both synthetic and real world images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang, S., Wei, E., Guan, R., et al.: Triangle chain codes for image matching. Neurocomputing 120(10), 268–276 (2013)
Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74(1), 59–73 (2007)
Han, J., Farin, D., de With, P.H.N.: A mixed-reality system for broadcasting sports video to mobile devices. IEEE Multimedia 18(2), 72–84 (2010)
Cheng, L., Gong, J., Li, M., et al.: 3D building model reconstruction from multi-view aerial imagery and Lidar data. Acta Geodaetica Cartogr. Sin. 77(2), 125–139 (2009)
Zhang, Z., Liang, X., Ganesh, A., Ma, Y.: TILT: Transform Invariant Low-Rank Textures. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010. LNCS, vol. 6494, pp. 314–328. Springer, Heidelberg (2011). doi:10.1007/978-3-642-19318-7_25
Zhang, Q., Li, Y., Blum, R.S., et al.: Matching of images with projective distortion using transform invariant low-rank textures. J. Vis. Commun. Image Represent. 38(C), 602–613 (2016)
Zhang, Y., Jiang, Z., Davis, L.S.: Learning structured low-rank representations for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 676–683. IEEE (2013)
Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. Neural Inf. Process. Syst. 24, 612–620 (2011)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv: 1511.06434 (2015)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: International Conference on International Conference on Machine Learning (ICML), pp. 807–814 (2010)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on International Conference on Machine Learning (ICML), pp. 448–456 (2015)
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoust models. In: International Conference on International Conference on Machine Learning (ICML) (2013)
Mao, X., Li, Q., Xie, H., et al.: Least squares generative adversarial networks. arXiv preprint arXiv:1611.04076 (2016)
Zhao, S.Y., Li, W.J.: Fast asynchronous parallel stochastic gradient decent. arXiv preprint arXiv:1508.05711 (2015)
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Neural Information Processing Systems, pp. 2017–2025 (2015)
Netzer, Y., Wang, T., Coates, A., et al.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning Unsupervised Feature Learning (2012)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 61271374).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhao, S., Li, J. (2017). Generating Low-Rank Textures via Generative Adversarial Network. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-70090-8_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70089-2
Online ISBN: 978-3-319-70090-8
eBook Packages: Computer ScienceComputer Science (R0)