Separability and Compactness Network for Image Recognition and Superresolution

IEEE Transactions on Neural Networks and Learning Systems

Volumn 30, Issue 11, 2019, Pages 3275-3286

  Zhou, Liguo ^a   Wang, Zhongyuan ^a   Luo, Yimin ^b   Xiong, Zixiang ^c  

^a WUHAN UNIVERSITY   (China)

^b KING'S COLLEGE LONDON   (United Kingdom)

^c Department of Electrical and Computer Engineering   (United States)

Author keywords

Convolutional neural networks (CNNs); cosine distance loss; image superresolution (SR); satellite video; softmax loss

Indexed keywords

CONVOLUTION; IMAGE RESOLUTION; JOB ANALYSIS; NEURAL NETWORKS; OPTICAL RESOLVING POWER; PERSONNEL TRAINING; SATELLITES; SOCIETIES AND INSTITUTIONS;

CONVOLUTIONAL NEURAL NETWORK; FACE; IMAGE SUPER-RESOLUTION; LOSS MEASUREMENT; TASK ANALYSIS;

FACE RECOGNITION;

ARTICLE; FACIAL RECOGNITION; SATELLITE IMAGERY;

EID: 85060931387     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2018.2890550     Document Type: Article

References (51)

1. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, pp. 436-444, May 2015.
2. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. NIPS, Lake Tahoe, NV, USA, 2012, pp. 1097-1105.
3. J. Xie, L. Xu, and E. Chen, "Image denoising and inpainting with deep neural networks," in Proc. NIPS, Lake Tahoe, NV, USA, 2012, pp. 341-349.
4. L. Zhang, L. Zhang, and B. Du, "Deep learning for remote sensing data: A technical tutorial on the state of the Art," IEEE Geosci. Remote Sens. Mag., vol. 4, no. 2, pp. 22-40, Jun. 2016.
5. C. Yan, H. Xie, S. Liu, J. Yin, Y. Zhang, and Q. Dai, "Effective Uyghur language text detection in complex background images for traffic prompt identification," IEEE Trans. Intell. Transp. Syst., vol. 19, no. 1, pp. 220-229, Jan. 2018.
6. C. Yan, H. Xie, J. Chen, Z. Zha, X. Hao, Y. Zhang, and Q. Dai, "A fast Uyghur text detector for complex background images," IEEE Trans. Multimedia, vol. 20, no. 12, pp. 3389-3398, Dec. 2018.
7. C. Yan et al., "Efficient parallel framework for HEVC motion estimation on many-core processors," IEEE Trans. Circuits Syst. Video Technol., vol. 24, no. 12, pp. 2077-2089, Dec. 2014.
8. H. Zhou, G. B. Huang, Z. Lin, H. Wang, and Y. C. Soh, "Stacked extreme learning machines," IEEE Trans. Cybern., vol. 45, no. 9, pp. 2013-2025, Sep. 2015.
9. F. J. Huang and Y. LeCun, "Large-scale learning with SVM and convolutional for generic object categorization," in Proc. CVPR, New York, NY, USA, Jun. 2006, pp. 284-291.
10. C. Yan, H. Xie, D. Yang, J. Yin, Y. Zhang, and Q. Dai, "Supervised hash coding with deep neural network for environment perception of intelligent vehicles," IEEE Trans. Intell. Transp. Syst., vol. 19, no. 1, pp. 284-295, Jan. 2018.
11. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
12. J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Netw., vol. 61, pp. 85-117, Jan. 2015.
13. Z. Shao, W. Wu, Z. Wang, W. Du, and C. Li, "SeaShips: A large-scale precisely annotated dataset for ship detection," IEEE Trans. Multimedia, vol. 20, no. 10, pp. 2593-2604, Oct. 2018.
14. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. CVPR, Las Vegas, NV, USA, Jun. 2016, pp. 770-778.
15. K. He, X. Zhang, S. Ren, and J. Sun, "Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification," in Proc. CVPR, Boston, MA, USA, Jun. 2015, pp. 1026-1034.
16. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, "DeepFace: Closing the gap to human-level performance in face verification," in Proc. CVPR, Columbus, OH, USA, Jun. 2014, pp. 1701-1708.
17. C. Dong, C. C. Loy, K. He, and X. Tang, "Image super-resolution using deep convolutional networks," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 295-307, Feb. 2015.
18. J. Kim, J. K. Lee, and K. M. Lee, "Accurate image super-resolution using very deep convolutional networks," in Proc. CVPR, Boston, MA, USA, Jun. 2015, pp. 1646-1654.
19. Y. Sun, Y. Chen, X. Wang, and X. Tang, "Deep learning face representation by joint identification-verification," in Proc. NIPS, Montreal, QC, Canada, 2014, pp. 1988-1996.
20. B. Du, W. Xiong, J. Wu, L. Zhang, L. Zhang, and D. Tao, "Stacked convolutional denoising auto-encoders for feature representation," IEEE Trans. Cybern., vol. 47, no. 4, pp. 1017-1027, Apr. 2016.
21. Q. Chen and V. Koltun, "Photographic image synthesis with cascaded refinement networks," in Proc. ICCV, Venice, Italy, 2017, pp. 1511-1520.
22. W. Zhou, S. Newsam, C. Li, and Z. Shao, "Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval," Remote Sens., vol. 9, no. 5, pp. 489-496, 2017.
23. W. Liu, Y. Wen, Z. Yu, and M. Yang, "Large-margin softmax loss for convolutional neural networks," in Proc. ICML, New York, NY, USA, 2016, pp. 507-516.
24. Y. Wen, K. Zhang, Z. Li, and Y. Qiao, "A discriminative feature learning approach for deep face recognition," in Proc. ECCV, Amsterdam, The Netherlands, 2016, pp. 499-515.
25. T. Zhang, "Solving large scale linear prediction problems using stochastic gradient descent algorithms," in Proc. ICML, Banff, AB, Canada, 2004, pp. 116-123.
26. C. K. I. Williams and D. Barber, "Bayesian classification with Gaussian processes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 12, pp. 1342-1351, Dec. 1998.
27. F. Schroff, D. Kalenichenko, and J. Philbin, "FaceNet: A unified embedding for face recognition and clustering," in Proc. CVPR, Boston, MA, USA, Jun. 2015, pp. 815-823.
28. Y. LeCun and C. Cortes. (2006). The MNIST Database of Handwritten Digits. [Online]. Available: http://yann.lecun.com/exdb/mnist/index.html
29. H. V. Nguyen and L. Bai, "Cosine similarity metric learning for face verification," in Proc. ACCV, Queenstown, New Zealand, Nov. 2010, pp. 709-720.
30. H. Wang, X. Gao, K. Zhang, and J. Li, "Single image super-resolution using Gaussian process regression with dictionary-based sampling and student-t likelihood," IEEE Trans. Image Process., vol. 26, no. 7, pp. 3556-3568, Jul. 2017.
31. J. Jiang, J. Ma, C. Chen, X. Jiang, and Z. Wang, "Noise robust face image super-resolution through smooth sparse representation," IEEE Trans. Cybern., vol. 47, no. 11, pp. 3991-4002, Nov. 2017.
32. D. Liu et al., "Learning temporal dynamics for video super-resolution: A deep learning approach," IEEE Trans. Image Process., vol. 27, no. 7, pp. 3432-3445, Jul. 2018.
33. Y. Huang, W. Wang, and L. Wang, "Video super-resolution via bidirectional recurrent convolutional networks," IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 1015-1028, Apr. 2018.
34. Z. Wang et al., "Multi-memory convolutional neural network for video super-resolution," IEEE Trans. Image Process., to be published, doi: 10.1109/TIP.2018.2887017.
35. W. Dong et al., "Hyperspectral image super-resolution via non-negative structured sparse representation," IEEE Trans. Image Process., vol. 25, no. 5, pp. 2337-2352, May 2016.
36. R. Timofte, V. De, and L. Van Gool, "Anchored neighborhood regression for fast example-based super-resolution," in Proc. ICCV, Sydney, NSW, Australia, Dec. 2013, pp. 1920-1927.
37. J. Sun, J. Zhu, and M. F. Tappen, "Context-constrained hallucination for image super-resolution," in Proc. CVPR, San Francisco, CA, USA, 2010, pp. 231-238.
38. O. Russakovsky et al., "ImageNet large scale visual recognition challenge," Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, Dec. 2015.
39. L. Zhou, R. Zhu, Y. Luo, S. Liu, and Z. Wang, "Improving convolutional neural networks via compacting features," in Proc. ICASSP, Apr. 2018, pp. 2946-2950.
40. Y. Luo, L. Zhou, S. Wang, and Z. Wang, "Video satellite imagery super resolution via convolutional neural networks," IEEE Geosci. Remote Sens. Lett., vol. 14, no. 12, pp. 2398-2402, Dec. 2017.
41. K. Jiang, Z. Wang, P. Yi, and J. Jiang, "A progressively enhanced network for video satellite imagery superresolution," IEEE Signal Process. Lett., vol. 25, no. 11, pp. 1630-1634, Nov. 2018.
42. M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi, "Lowcomplexity single-image super-resolution based on nonnegative neighbor embedding," in Proc. BMVC, Guildford, U.K., 2012, pp. 1-10.
43. R. Zeyde, M. Elad, and M. Protter, "On single image scale-up using sparse-representations," in Proc. Int. Conf. Curves Surfaces. Berlin, Germany: Springer, 2010, pp. 711-730.
44. D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," in Proc. ICCV, Vancouver, BC, Canada, Jul. 2001, pp. 416-423.
45. A. Krizhevsky. (2009). Learning Multiple Layers Of Features From Tiny Images. [Online]. Available: http://www.cs.toronto.edu/ kriz/cifar.html
46. Y. Jia et al., "Caffe: Convolutional architecture for fast feature embedding," in Proc. ACM Multimedia, Orlando, FL, USA, 2014, pp. 675-678.
47. V. Nair and G. E. Hinton, "Rectified linear units improve restricted Boltzmann machines," in Proc. ICML, Haifa, Israel, 2010, pp. 807-814.
48. O. M. Parkhi, A. Vedaldi, and A. Zisserman, "Deep face recognition," in Proc. BMVC, Swansea, U.K., 2015, pp. 1-12.
49. G. B. Huang and E. Learned-Miller, "Labeled faces in the wild: Updates and new reporting procedures," Dept. Comput. Sci., Univ. Massachusetts Amherst, Amherst, MA, USA, Tech. Rep. 14-003, 2014.
50. D. Yi, Z. Lei, S. Liao, and S. Z. Li. (2014). "Learning face representation from scratch." [Online]. Available: https://arxiv.org/abs/1411.7923
51. J.-B. Huang, A. Singh, and N. Ahuja, "Single image super-resolution from transformed self-exemplars," in Proc. CVPR, Boston, MA, USA, Jun. 2015, pp. 5197-5206.