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| Title: | 使用深度學習識別現代畫中的藝術風格-以台灣鄉土風格繪畫為例
Identifying Artistic Styles in Modern Painting Using Deep Learning- Taiwan's local style painting as an Example |
| Authors: | 陳亦霖
Chen, I-Lin |
| Contributors: | 彭彥璁
Peng, Yan-Tsung
陳亦霖
Chen, I-Lin |
| Keywords: | 深度學習
圖像分類
台灣鄉土風格繪畫
資料擴增
Deep learning
Image classification
Taiwan's local style painting
Data augmentation |
| Date: | 2024 |
| Issue Date: | 2024-03-01 14:12:04 (UTC+8) |
| Abstract: | 繪畫風格分類一直是一個活躍的研究領域，但對於國內繪畫風格透過深度學習分析的研究相當稀少，故本研究旨在透過機器學習協助現代藝術中有定義的國別風格主義分類與台灣鄉土繪畫風格一同進行分類探討，其中台灣鄉土繪畫樣本為本研究所收集從1860年代至1970年代這段時期內台灣知名前輩創作的本土鄉情藝術作品，以此做為台灣鄉土藝術風格繪畫的指標，期間台灣知名畫家有陳澄波、廖繼春、李梅樹、楊三郎、李石樵等人的繪畫作品，本研究稱為台灣鄉土風格繪畫，與WikiArt之現代藝術中可稱為國別的藝術主義風格一同進行監督式學習，並探討擴增資料集、訓練資料特性及分類器學習模效能之評分及人文歷史角度探討關聯。在本文中會利用常見的4種深度學習模型AlexNet、VGG19、GoogleNet、ResNet152，用以辨識和分類藝術風格，藝術風格分為美國寫實主義(American Realism)、日本主義(Japonism)、墨西哥壁畫運動(Muralism) 、印度空間畫(Indian Space painting)、巴洛克復興風格(Neo-baroque)、台灣鄉土藝術(Tw-Local) 等6種不同類型，透過資料集擴增處理及各模型選擇之實驗，最終以ResNet152模型分類準確率達到93%之表現，說明本研究定義的分類藝術風格可透過模型加以分類，並透過人文歷史角度探討深度學習模型所識別的繪畫特徵關聯做說明。
Painting style classification has always been an active research field, but research on domestic painting styles analyzed through deep learning is rare, This study aims to contribute to the classification of national mannerism in modern art by exploring the classification of Taiwanese vernacular painting style through machine learning. Among the aspects discussed, the samples of Taiwanese local paintings are local nostalgic works of art collected by the Institute from the 1860s to the 1970s, created by well-known predecessors in Taiwan, this can be taken as an indicator of Taiwan's local art style painting,during this period, well-known Taiwanese painters Chen Chengbo, Liao Jichun, Li Meishu, Yang Sanlang, Li Shiqiao and others produced their paintings. This study titled ‘Tw-Local style painting’ employs supervised learning to analyze what may be referred to as national artistic styles found in WikiArt's Modern Art.It also discuss the relationship between augmented data sets, training data characteristics and classifier learning model performance scores from a humanistic and historical perspective. In the text, we will use the four common deep learning models AlexNet, VGG19, GoogleNet, ResNet152. These models will be used to identify and classify artistic styles, which are divided into American Realism, Japonism, Muralism, Indian Space painting, and Neo-baroque, Taiwan local art (Tw-Local) and other 6 different types.The experiments involve data augmentation processing and selection of each model. Finally, using the ResNet152 model, the classification accuracy rate reached 93%. This result indicates that the categorical artistic styles defined in this study can be classified through the model and explains the correlation of painting features identified by the deep learning model from the perspective of humanities and history. |
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| Description: | 碩士
國立政治大學
資訊科學系碩士在職專班
109971022 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109971022 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系碩士在職專班] 學位論文
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