Cite

1. Bonnet, P., A. Joly, H. Goëau, J. Champ, C. Vignau, J. F. Molino, D. Barthélémy, N. Boujemaa. Plant Identification: Man vs. Machine. – Multimedia Tools and Applications, Vol. 75, 2016, No 3, pp. 1647-1665.10.1007/s11042-015-2607-4Search in Google Scholar

2. Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna. Rethinking the Inception Architecture for Computer Vision. – arXiv preprint arXiv:1512.00567, 2015.Search in Google Scholar

3. Champ, J., T. Lorieul, M. Servajean, A. Joly. A Comparative Study of Fine-Grained Classification Methods in the Context of the Lifeclef Plant Identification Challenge 2015. – In: Working Notes of CLEF 2015 Conference, 2015.Search in Google Scholar

4. Chen, Q., M. Abedini, R. Garnavi, X. Liang. IBM Research Australia at LifeCLEF2014: Plant Identification Task. – In: Working Notes of CLEF 2014 Conference, 2014., pp. 693-704.Search in Google Scholar

5. Cope, J. S., D. Corney, J. Y. Clark, P. Remagnino, P. Wilkin. Plant Species Identification Using Digital Morphometrics: A Review. – Expert Systems with Applications, Vol. 39, 2012, No 8, pp. 7562-7573.10.1016/j.eswa.2012.01.073Search in Google Scholar

6. Duchi, J., E. Hazan, Y. Singer. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. – The Journal of Machine Learning Research, Vol. 12, 2011, pp. 2121-2159.Search in Google Scholar

7. Ge, Z., C. Mccool, P. Corke. Content Specific Feature Learning for Fine-Grained Plant Classification. – In: Working Notes of CLEF 2015 Conference, 2015.10.1109/CVPRW.2015.7301271Search in Google Scholar

8. Göeau, H., A. Joly, B. Pierre. LifeCLEF Plant Identification Task 2015. – CLEF Working Notes, 2015.Search in Google Scholar

9. He, K., X. Zhang, S. Ren, J. Sun. Deep Residual Learning for Image Recognition. – In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.10.1109/CVPR.2016.90Search in Google Scholar

10. He, K., X. Zhang, S. Ren, J. Sun. Delving Deep into Rectifiers: Surpassing Human-Level Performance on Imagenet Classification. – In: Proc. of IEEE International Conference on Computer Vision, 2015, pp. 1026-1034.Search in Google Scholar

11. Ioffe, S., C. Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. – arXiv preprint arXiv:1502.03167, 2015.Search in Google Scholar

12. Kadir, A., L. E. Nugroho, A. Susanto, P. I. Santosa. Leaf Classification Using Shape, Color, and Texture Features. – International Journal of Computer Trends and Technology, Vol. 1, July-August 2011, No 3, pp. 306-311.10.5121/ijcsit.2011.3318Search in Google Scholar

13. Krizhevsky, A., I. Sutskever, G. E. Hinton. Imagenet Classification with Deep Convolutional Neural Networks. – In: Advances in Neural Information Processing Systems, 2012, pp. 1097-1105.Search in Google Scholar

14. Kumar, N., P. N. Belhumeur, A. Biswas, D. W. Jacobs, W. J. Kress, I. C. Lopez, J. V. Soares. Leafsnap: A Computer Vision System for Automatic Plant Species Identification. – In: Computer Vision ECCV’2012. Berlin, Heidelberg, Springer, 2012, pp. 502-516.Search in Google Scholar

15. LeCun, B. B., J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel. Handwritten Digit Recognition with a Back-Propagation Network. – In: Advances in Neural Information Processing Systems, Vol. 2, 1990, pp. 396-404.Search in Google Scholar

16. Le, T. L., D. N. Dng, H. Vu, T. N Nguyen. Mica at Lifeclef 2015: Multi-Organ Plant Identification. – In: Working Notes of CLEF 2015 Conference, 2015.Search in Google Scholar

17. LeCun, Y., L. Bottou, G. Orr, K. Muller. Efficient Backprop. – In: G. Orr, K. Muller, Eds. Neural Networks: Tricks of the Trade. Springer, 1998, pp. 9-48.Search in Google Scholar

18. Long, X., H. Lu, Y. Peng, X. Wang, S. Feng. Image Classification Based on Improved VLAD. – Multimedia Tools and Applications, Vol. 75, 2016, Issue 10, pp. 5533-5555.10.1007/s11042-015-2524-6Search in Google Scholar

19. Maas, A. L., Y. H. Awni, A. Y. Ng. Rectifier Nonlinearities Improve Neural Network Acoustic Models. – In: Proc. ICML, Vol. 30, 2013, p. 1.Search in Google Scholar

20. Sermanet, P., D. Eigen, X. Zhang, M. Mathieu, R. Fergus, Y. LeCun. Overfeat: Integrated Recognition, Localization and Detection Using Convolutional Networks. – arXiv preprint arXiv:1312.6229, 2013.Search in Google Scholar

21. Reyes, A. K., J. C. Caicedo, J. E. Camargo. Fine-Tuning Deep Convolutional Networks for Plant Recognition. – In: Working Notes of CLEF 2015 Conference, 2015.Search in Google Scholar

22. Robertson, S. A New Interpretation of Average Precision. – In: Proc. of 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2008, pp. 689-690.10.1145/1390334.1390453Search in Google Scholar

23. Russakovsky, O., J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Hunag, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, L. Fei-Fei. Imagenet Large Scale Visual Recognition Challenge. – International Journal of Computer Vision, Vol. 115, 2015, No 3, pp. 211-252.10.1007/s11263-015-0816-ySearch in Google Scholar

24. Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. – The Journal of Machine Learning Research, Vol. 15, 2014, No 1, pp. 1929-1958.Search in Google Scholar

25. Sünderhauf, N., C. McCool, B. Upcroft, T. Perez. Fine-Grained Plant Classification Using Convolutional Neural Networks for Feature Extraction. – In: Working Notes of CLEF 2014 Conference, 2014., pp. 756-762.Search in Google Scholar

26. Sungbin, C. Plant Identification with Deep Convolutional Neural Network: SNUMedinfo at LifeCLEF Plant Identification Task 2015. – In: Working Notes of CLEF 2015 Conference, 2015.Search in Google Scholar

27. Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich. Going Deeper with Convolutions. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1-9.10.1109/CVPR.2015.7298594Search in Google Scholar

28. Szegedy, C., S. Ioffe, V. Vanhoucke. Inception-v4, Inception-Resnet and the Impact of Residual Connections on Learning. – arXiv Preprint arXiv:1602.07261, 2016.Search in Google Scholar

29. Szűcs, G., D. Papp, D. Lovas. Viewpoints Combined Classification Method in Image-Based Plant Identification Task. – In: L. Cappellato, N. Ferro, M. Halvey, W. Kraaij, Eds. Working Notes for CLEF 2014 Conference, Sheffield, Great Britain, Vol. 1180, 15-18 September 2014, pp. 763-770.Search in Google Scholar

30. Nair, V., G. E. Hinton. Rectified Linear Units Improve Restricted Boltzmann Machines. – In: Proc. of 27th International Conference on Machine Learning, 2010, pp. 807-814.Search in Google Scholar

31. Wu, S. G., F. S. Bao, E. Y. Xu, Y. X. Wang, Y. F. Chang, Q. L. Xiang. A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network. – In: IEEE International Symposium on Signal Processing and Information Technology, 2007, pp. 11-16.10.1109/ISSPIT.2007.4458016Search in Google Scholar

32. Zeiler, M. D. ADADELTA: An Adaptive Learning Rate Method. – arXiv preprint arXiv:1212.5701, 2012.Search in Google Scholar

eISSN:
1314-4081
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology