ARCHIVES

1. Yo-Ping Huang and Haobijam Basanta, “Bird Image Retrieval and Recognition Using a Deep Learning Platform”, IEEE Access, Vol. 7,
May 2019.
2. 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,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211252, Dec. 2015.
3. H. Yao, S. Zhang, Y. Zhang, J. Li, and Q. Tian, “Coarse-to-ne description for ne-grained visual categorization,” IEEE Trans. Image Process.,
vol. 25, no. 10, pp. 48584872, Oct. 2016.
4. F. Garcia, J. Cervantes, A. Lopez, and M. Alvarado, “Fruit classication by extracting color chromaticity, shape and texture features:
Towards an application for supermarkets,” IEEE Latin Amer. Trans., vol. 14, no. 7, pp. 34343443, Jul. 2016.
5. L. Zhu, J. Shen, H. Jin, L. Xie, and R. Zheng, “Landmark classication with hierarchical multi-modal exemplar feature,” IEEE Trans.
Multime- dia, vol. 17, no. 7, pp. 981993, Jul. 2015.
6. X. Liang, L. Lin, W. Yang, P. Luo, J. Huang, and S. Yan, “Clothes co- parsing via joint image segmentation and labeling with application
to clothing retrieval,” IEEE Trans. Multimedia, vol. 18, no. 6, pp. 11751186, Jun. 2016.
7. Y.-P. Huang, L. Sithole, and T.-T. Lee, “Structure from motion technique for scene detection using autonomous drone navigation,” IEEE
Trans. Syst., Man, Cybern., Syst., to be published.
8. C. McCool, I. Sa, F. Dayoub, C. Lehnert, T. Perez, and B. Upcroft, “Visual detection of occluded crop: For automated harvesting,” in
Proc. Int. Conf. Robot. Autom. (ICRA), Stockholm, Sweden, May 2016, pp. 25062512.
9. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classication with deep convolutional neural networks,” in Proc. 25th Int. Conf.
Advance Neural Inf. Process. Syst., Lake Tahoe, NV, USA, Dec. 2012, pp. 10971105.
10. Jie Xie, Kai Hu, Mingying Zhu, Jinghu Yu, Qibing Zhu, “Investigation of Different CNN-Based Models for Improved Bird Sound
Classification”, IEEE Access, vol. 7, pp. 175353-175361, 2019.
11. Marini, A., Turatti, A. J., Britto, A. S., Koerich, A. L. (2015). Visual and acoustic identification of bird species. 2015 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP) (2015).
12. “Bird Species Categorization Using Pose Normalized Deep Convolu- tional Net” Steve Branson, Grant Van Horn, Serge Belongie ,Pietro
Peron (2015).
13. Li Liu, W. Ouyang, X. Wang, P. Fieguth, X. Liu, and M. Pietika¨inen, “Deep learning for generic object detection: A survey,” Sep. 2018,
arXiv:1809.02165. [Online]. Available: https://arxiv.org/abs/1809.02165
14. K. Dhindsa, K. D. Gauder, K. A. Marszalek, B. Terpou, and S. Becker, “Progressive thresholding: Shaping and specicity in automated
neurofeedback training,” IEEE IEEE Trans. Neural Syst. Rehabil. Eng., vol. 26, no. 12, pp. 22972305, Dec. 2018.
15. C.-Y. Lee, A. Bhardwaj,W. Di, V. Jagadeesh, and R. Piramuthu, “Region- based discriminative feature pooling for scene text recognition,”
in Proc. Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 40504057.
16. B. Hariharan, P. Arbela´ez, R. Girshick, and J. Malik, “Simultaneous detection and segmentation,” in Proc. Eur. Conf. Comput. Vis., Jul.
2014, pp. 297312.
17. S. Branson, G. V. Horn, S. Belongie, and P. Perona, “Bird species categorization using pose normalized deep convolutional nets,” in.
Proc. Brit. Mach. Vis. Conf., Nottingham, U.K., Jun. 2014, pp. 114.
18. Stavros Ntalampiras, “Bird species identification via transfer learning from music genres”, Ecological Informatics, Vol. 44, March 2018.
19. Loris Nanni, Yandre M. G. Costa, Rafael L. Aguiar, Rafael B. Mangolin, Sheryl Brahnam and Carlos N. Silla Jr., “Ensemble of
convolutional neural networks to improve animal audio classification”, EURASIP Journal on Audio, Speech, and Music Processing,
Article number: 8(2020), May 2020.
20. A. Krizhevsky, I. Sutskever, G. E. Hinton, “Imagenet classification with deep convolutional neural networks”, Advances in Neural
Information Processing Systems, pp. 1097–1105, 2012.
21. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan,
22. V. Vanhoucke, A. Rabinovich, “Going deeper with convolutions”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
pp. 1–9, 2015.
23. K. Simonyan, A. Zisserman, “Very deep convolutional networks for large-scale image recognition”, arXiv preprint (2014).
arXiv:1409.1556.
24. J. Bruna, S. Mallat, “Invariant scattering convolution networks”, IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1872–1886 (2013).
25. L. Sifre, S. Mallat, “Combined scattering for rotation invariant texture analysis”, ESANN, vol. 44, pp. 68–81, 2012.
26. Jiaohua Qin, Wenyan Pan, Xuyu Xiang, Yun Tan, Guimin Hou, “A biological image classification method based on improved CNN ”,
Ecological Informatics, Vol. 58, July 2020.
27. C. Wah et al. The Caltech-UCSD Birds-200-2011 Dataset. Tech. rep. CNS-TR-2011-001. California Institute of Technology, 2011.
28. J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, Q. V. Le, and A. Y. Ng, “Large
scale distributed deep networks,” in Proc. 25th Int. Conf. Adv. Neural Inf. Process. Syst., Lake Tahoe, NV, USA, Dec. 2012, pp. 12231231.
29. L. Yang, A. M. MacEachren, P. Mitra, and T. Onorati, “Visually-enabled active deep learning for (Geo) text and image classication: A
review,” Int. J. Geo-Inf., vol. 7, no. 2, p. 65, Feb. 2018.
30. J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks,” in Proc. Int. Conf. Advance
Neural Inf. Process. Syst., Dec. 2014, pp. 33203328.
31. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. Int. Conf. Comput. Vis. Pattern Recognit.,
Jun. 2016, pp. 770778.
32. N. Zhang, J. Donahue, R. Girshick, and T. Darrell, “Part-based R- CNNs for ne-grained category detection,” in Proc. Int. Conf. Eur.
Conf. Comput. Vis., Cham, Switzerland, Jul. 2014, pp. 834849.
33. C. Huang, Z. He, G. Cao, and W. Cao, “Task-driven progressive part localization for ne-grained object recognition,” IEEE Trans.
Multimedia, vol. 18, no. 12, pp. 23722383, Dec. 2016.