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| Title: | 深度學習於國畫主題辨識之應用
Identifying Chinese painting genres with deep learning |
| Authors: | 許嘉宏
Hsu, Chia-Hung |
| Contributors: | 蔡炎龍
Tsai, Yen-Lung
許嘉宏
Hsu, Chia-Hung |
| Keywords: | 深度學習
卷積神經網路
影像辨識
Deep Learning
Nerural Network
CNN
Image Recognition |
| Date: | 2019 |
| Issue Date: | 2019-08-07 16:35:21 (UTC+8) |
| Abstract: | 本篇文章主要使用卷積神經網路來進行圖像辨識，資料來源用台北故宮 博物院線上資料庫，其中圖像收藏量三萬筆，本篇將範圍縮小至畫軸的部 分，總計有 4 千筆，因為每張圖像有主要主題跟次要主題，無法直接用卷 積神經網路來分類。所以先利用 SLIC 演算法將圖像分割，再來進行標籤及 訓練模型。最後如有新的作品要進行辨識，也進行同樣分割，用模型辨識 後，再統整結果得到此作品有哪些主題性。
In this paper, we want to recognize one image with multiple genres. We collected data from National Palace Museun. If we just use traditional CNN to recognize it, we only get one genre with one image. Hence, we segment image with SLIC algorithm. It can segment image into fixed size with similar range, then we can use them to train the model. After training, if we get the new image, we can use SILC algorithm with same parameter and put it in the model. Then we can recognize this new image with multiple genres. |
| Reference: | [1]RadhakrishnaAchanta,AppuShaji,KevinSmith,AurelienLucchi,PascalFua,andSabine Süsstrunk. Slic superpixels, 2010.
[2] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473, 2014.
[3] John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res., 12:2121–2159, 2011.
[4] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR, abs/ 1311.2524, 2013.
[5] J. B. Heaton, N. G. Polson, and J. H. Witte. Deep learning in finance. CoRR, abs/ 1602.06561, 2016.
[6] Donald Hebb. The The Organization of Behavior. 1949.
[7] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou, and Kilian Q. Weinberger, editors, NIPS, pages 1106–1114, 2012.
[8] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097– 1105. Curran Associates, Inc., 2012.
[9] Jan Kukačka, Vladimir Golkov, and Daniel Cremers. Regularization for deep learning: A taxonomy, 2017.
[10]S.Lawrence,C.L.Giles,AhChungTsoi,andA.D.Back.Facerecognition:aconvolutional neural-network approach. Neural Networks, IEEE Transactions on, 8(1):98–113, January 1997.
[11] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553): 436–444, may 2015.
[12] Min Lin, Qiang Chen, and Shuicheng Yan. Network in network, 2013.
[13] National Palace Museun. 書畫典藏資料檢索系統, 2019.
[14] Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. IEEE Trans. on Knowl. and Data Eng., 22(10):1345–1359, October 2010.
[15] Tara N. Sainath, Abdel rahman Mohamed, Brian Kingsbury, and Bhuvana Ramabhadran. Deep convolutional neural networks for lvcsr. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, 2013.
[16] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations, 2015.
[17] Jonathan J Tompson, Arjun Jain, Yann LeCun, and Christoph Bregler. Joint training of a convolutional network and a graphical model for human pose estimation. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 1799–1807. Curran Associates, Inc., 2014.
[18] H. Y. Xiong, B. Alipanahi, L. J. Lee, H. Bretschneider, D. Merico, R. K. C. Yuen, Y. Hua, S. Gueroussov, H. S. Najafabadi, T. R. Hughes, Q. Morris, Y. Barash, A. R. Krainer, N. Jojic, S. W. Scherer, B. J. Blencowe, and B. J. Frey. The human splicing code reveals new insights into the genetic determinants of disease. Science, 347(6218):1254806– 1254806, dec 2014. |
| Description: | 碩士
國立政治大學
應用數學系
104751003 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0104751003 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU201900448 |
| Appears in Collections: | [應用數學系] 學位論文
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