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https://ah.lib.nccu.edu.tw/handle/140.119/124868| 題名: | 深度學習於國畫主題辨識之應用 Identifying Chinese painting genres with deep learning |
作者: | 許嘉宏 Hsu, Chia-Hung |
貢獻者: | 蔡炎龍 Tsai, Yen-Lung 許嘉宏 Hsu, Chia-Hung |
關鍵詞: | 深度學習 卷積神經網路 影像辨識 Deep Learning Nerural Network CNN Image Recognition |
日期: | 2019 | 上傳時間: | 7-Aug-2019 | 摘要: | 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. |
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資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0104751003 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
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| 100301.pdf | 13.04 MB | Adobe PDF2 | View/Open |
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