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| 研究生： |
許嘉宏 Hsu, Chia-Hung |
|---|---|
| 論文名稱： |
深度學習於國畫主題辨識之應用 Identifying Chinese painting genres with deep learning |
| 指導教授： |
蔡炎龍
Tsai, Yen-Lung |
| 口試委員: |
陳天進
張宜武 |
| 學位類別： |
碩士
Master |
| 系所名稱： |
理學院 - 應用數學系 Department of Mathematical Sciences |
| 論文出版年： | 2019 |
| 畢業學年度： | 107 |
| 語文別： | 英文 |
| 論文頁數： | 65 |
| 中文關鍵詞： | 深度學習 、卷積神經網路 、影像辨識 |
| 外文關鍵詞： | Nerural Network |
| DOI URL： | http://doi.org/10.6814/NCCU201900448 |
| 相關次數： | 點閱：117 下載：27 |
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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.
第一章 Introduction 1
第二章 Deep Learning 3
第一節 Neurons and Neural Networks 4
第二節 Activation Function 7
第三節 Loss Function 9
第四節 Gradient Descent Method 11
第三章 Convolutional Neural Network 13
第一節 Convolution Layer 13
第二節 Pooling Layer 22
第四章 Data Collection and Processing 25
第一節 K-means Clustering 25
第二節 SLIC Algorithm 27
第三節 Make the Labels 29
第五章 Model Construction 33
第一節 Models Structure 33
第二節 Transfer Learning 37
第三節 Imbalance Data 38
第四節 Result 38
第六章 Conclusion 41
Appendix A Python Script 42
A.1 Segment the Painting 42
A.2 Create the Label 45
A.3 Train the Model with the Model D 47
A.4 Train the model with Transfer Learning 50
A.5 GUI 53
Bibliography 64
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