基於卷積神經網路特徵與個人化模組之 畫作推薦系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：101

、訪客IP：3.22.217.242

姓名

李奇庭(CHI-TING LEE) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於卷積神經網路特徵與個人化模組之畫作推薦系統
(Art Work Recommendation System with Personalized Modules based on Convolutional Neural Networks)

相關論文

★ 影片指定對象臉部置換系統	★ 以單一攝影機實現單指虛擬鍵盤之功能
★ 基於視覺的手寫軌跡注音符號組合辨識系統	★ 利用動態貝氏網路在空照影像中進行車輛偵測
★ 以視訊為基礎之手寫簽名認證	★ 使用膚色與陰影機率高斯混合模型之移動膚色區域偵測
★ 影像中賦予信任等級的群眾切割	★ 航空監控影像之區域切割與分類
★ 在群體人數估計應用中使用不同特徵與回歸方法之分析比較	★ 以視覺為基礎之強韌多指尖偵測與人機介面應用
★ 在夜間受雨滴汙染鏡頭所拍攝的影片下之車流量估計	★ 影像特徵點匹配應用於景點影像檢索
★ 自動感興趣區域切割及遠距交通影像中的軌跡分析	★ 基於回歸模型與利用全天空影像特徵和歷史資訊之短期日射量預測
★ Analysis of the Performance of Different Classifiers for Cloud Detection Application	★ 全天空影像之雲追蹤與太陽遮蔽預測

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

人工智慧與深度學習領域被廣泛應用在各個領域中，舉凡車輛辨識、AlphaGo、AOI自動光學檢測、自然語言處理、圖形識別等。近日閒暇時光，欣賞畫作培養興趣的人日漸增加，以及畫作電子化建檔已成為傳統藝術館的新興趨勢。所以我們提供一個應用程式，藉由擷取畫作的特徵，再透過特徵的相似性進行排序，從而使使用者能獲得資料庫中與其畫作風格相近的作品。
本論文使用卷積神經網路-VGG16架構，其模組的訓練資料為ImageNet大約120萬張圖作為訓練。而本論文將畫作當作測試資料，並取得第一層全連接層的權重當作是每張圖獨立的特徵向量再訓練。並且為了加速系統運作，使用主成分分析(PCA)進行一致性的降維。由於每個人對於畫作的風格具有主觀的意識，因此自行設計出一個評價應用程式(Feedback application)，經由不斷的訓練畫作的權重，讓系統濾掉使用者不喜歡的畫作並且依個人喜好推播出相關畫作。

摘要(英)

Recent years, artificial intelligence and deep learning are widely used in various fields. For example, vehicle identification, AlphaGo, AOI automatic optical detection, natural language processing, graphic recognition, etc. The number of people visiting the art exhibition at their free time has been increased in recent year,and the digit of paintings has become an emerging trend in traditional art galleries. So we provide an application that captures the features of the paintings and then sorts them by the similarity of the features so that users can obtain works in the database that are similar in style to their paintings.

The propose system uses the convolutional neural network-VGG16 architecture. The module training data is about 1.2 million images of ImageNet as training. In this paper, the paintings are used as test data, and the weights of the first layer of the full-connected layer is obtained as an independent feature vector retraining for each image. And in order to speed up the system operation, principal component analysis (PCA) is used for consistent dimensionality reduction.

Since everyone has a subjective awareness of the style of the paintings, our system designed a feedback application. Through the weights of the continuous training paintings, the system filters out the paintings that the user does not like and pushes the related paintings according to personal preference.

關鍵字(中)

★ 深度學習
★ 卷積神經網路
★ VGG-16
★ 主成分分析(PCA)
★ 回饋系統

關鍵字(英)

★ Deep Learning
★ CNN
★ VGG-16
★ PCA
★ Feedback Application

論文目次

摘要 IV
Abstract V
致謝 VI
目錄 VII
圖目錄 IX
表目錄 X
第一章緒論 1
1.1 研究動機 1
1.2 相關文獻 3
1.3 系統架構介紹 5
1.4 論文架構 8
第二章背景知識與方法研究 9
2.1 ImageNet 9
2.2 卷積神經網路 10
2.2.1模型基本架構 10
2.2.2 卷積層(convolutional layer) 11
2.2.3 最大池層(Max-pooling layer) 13
2.2.4 ReLU Nonlinearity[16] 13
2.2.5 Local Response Normalization 16
2.2.6 Reducing Overfitting 17
2.3 VGG-16 卷積網路模型 18
2.3.1 Keras API 18
2.3.2 VGG-16 架構 19
2.4 PCA主成分分析 21
第三章研究方法與系統程式 23
3.1 畫作特徵取得與處理 23
3.1.1 資料預處理 23
3.1.2 建立VGG-16網路模型 25
3.1.3 權重特徵提取 26
3.1.4 PCA降維 27
3.2 畫作排序和比較特徵相似性方法 29
3.2.1畫作特徵權重 29
3.2.2 畫作特徵排序與相似性比對 30
3.3 使用者的回饋資料 31
3.4 個人化模組 32
3.5 系統介面功能說明 35
第四章實驗結果 37
4.1 畫作資料庫的蒐集 37
4.2 實驗結果與分析 39
4.2.1 實驗一 : 成功推播出至少一張相同標籤畫作的比例 39
4.2.2 實驗二 : PCA降維對推播結果的影響 42
4.2.3 實驗三 : 實驗結果分析比較 43
4.2.4 實驗四 : 回饋系統實驗結果 45
4.2.5 實驗五 : 未經過使用者評分畫作比較 47
4.2.6 實驗六 : 個人化模組濾掉使用者評價0分畫作的比例 49
4.2.7 實驗七 : 各層卷積神經網路特徵比較 50
第五章結論與未來研究方向 51
參考文獻 52

參考文獻

[1] C. R. Johnson, E. Hendriks, I. J. Berezhnoy, E. Brevdo, S. M. Hughes, I. Daubechies, J. Li, E. Postma, and J. Z.Wang. “Image processing for artist identification.” IEEE Signal Processing Magazine.Process.25(4), 2008.

[2] Jia Li, Lei Yao, Ella Hendriks, James Z. Wang, “Rhythmic Brushstrokes Distinguish van Gogh from His Contemporaries: Findings via Automated Brushstroke Extraction,” IEEE. Trans. Pattern Analysis and Machine Intelligence, vol.34, no.6, pp.1159 – 1176, June 2012.

[3] S. Agarwal, H. Karnick, N. Pant, and U. Patel. “Genre and style based painting classification.” In Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on. pp.588–594, 2015.

[4] Eva Cetinic, Sonja Grgic , “Automated Painter Recognition Based on Image,” in 55th International Symposium ELMAR-2013, Zadar, Croatia, pp.19-22 , Sept.25-27, 2013.

[5] Qiusi Wang , Feng Gao , Yitong Wang, Ling-Yu Duan ,” Adaptive Weighted Matching of Deep Convolutional Features for Painting Retrieval “, IEEE Second International Conference on Multimedia Big Data, 2016.

[6] Yaniv Bar, Noga Levy, Lior Wolf,” Classification of Artistic Styles using Binarized Features Derived from a Deep Neural Network” ECCV 2014: Computer Vision - ECCV Workshops, pp 71-84 , 2014.

[7] Jou, J., Agrawal, S.”Artist identification for renaissance paintings”

[8] Karayev, S., Hertzmann, A., Winnemoeller, H., Agarwala, A., Darrell,T.”Recognizing image style.” arXiv preprint arXiv:1311.3715 ,2013.

[9] Krizhevsky, A., Sutskever, I., Hinton, G.E.” ImageNet classification with deep convolutional neural networks.” Advances in neural information processing systems. pp. 1097-1105 ,2012.

[10] Meijun Sun, Dong Zhang, Jinchang Ren, Zheng Wang, Jesse S.Jin, “ Brushstroke based sparse hybrid convolutional neural networks for author classification of Chinese ink-wash paintings,” in 2015 IEEE International Conference on Image Processing (ICIP), QC, Canada, Sept.27-30, pp.626-630. , 2015,

[11] 廖育萱,”融合多特徵與個人化模組之畫作推薦系統 ” 國立中央大學,資訊工程研究所, 2017。

[12] Karen Simonyan, Andrew Zisserman” Very Deep Convolutional Networks For Large-Scale Image Recognition” ,Published as a conference paper at ICLR 2015

[13] A. Krizhevsky, I. Sutskever, G. E. Hinton, “ImageNet classification with deep convolutional neural networks.” Advances in Neural Information Processing Systems 25, pp.1097-1105, 2012.

[14] D.C. Cire﹐san, U. Meier, J. Masci, L.M. Gambardella, and J. Schmidhuber. “High-performance neural networks for visual object classification.”, Arxiv preprint arXiv:1102.0183, 2011.

[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei “ImageNet: A Large-Scale Hierarchical Image Database”, IEEE International Conference on Computer Vision and Pattern Recognition(ICPR), 2009.

[16] Vinod Nair, Geoffrey E. Hinton,”Rectified Linear Units Improve Restricted Boltzmann Machines”, In Proc. 27th International Conference on Machine Learning, 2010.

[17] Pearson, K.,”On Lines and Planes of Closest Fit to Systems of Points in Space.”, Philosophical Magazine. 2 (6): 559–572, 1901.

指導教授

鄭旭詠

審核日期

2018-7-23

推文