融合多特徵與個人化模組之畫作推薦系統

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姓名

廖育萱(Yu-Syuan Liao) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

融合多特徵與個人化模組之畫作推薦系統
(Art work recommendation system with fusion of multiple features and personalized modules)

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摘要(中)

科技日新月異的發展，影像處理 (Image Processing)被廣泛的應用在各個領被廣泛的應用在各個領域中，車輛偵測、圖形識別人臉辨等。近日來利用空閒時欣賞畫作的們亦日漸趨多，但目前較少能直接使用圖片影像為輸入進行畫作相似風格檢索我們提供一個應用程式，藉由擷取畫作的特徵再透過相關性排序(Rank)後，即可推播出資料庫中與詢問的畫作相似風格給予使用者。後，即可推播出資料庫中與詢問的畫作相似風格給予使用者。
本論文使用紋理 (Texture)、顏色 (Color histogram)、Canny邊緣偵測 (Canny edge detection)、Sobel邊緣偵測的梯度強分佈特性 (Sobel gradient distribution magnitude)等方式作為畫作的特徵。由於每個人對畫作的風格為主觀意識，因此自行設計出一個評價應用程式 (Feedback application)，經由此應用程式可獲得回饋資料作為個人化模組 (Personalized module)的訓練資料，經由畫作特徵的

摘要(英)

The number of people visiting the art exhibition at their free time has been increased in recent years. People might be interested of a certain artwork and would like to know if there are any other artworks with similar styles. However, artwork recommendation systems that can retrieve images with similar styles are rare in the existing research works. We propose an application that can utilize the features extracted from the art works and rank the artworks by the features of the relevance and then return artworks with similar styles in our database with the query image of art work to the users.
The proposed system utilizes gradient distribution, colors, texture, and face information as the features of the art works. Everyone has a personal opinion about the artworks, so we also propose a feedback mechanism. We can obtain the feedback data and the feedback data as the training data of personalized modules and train the weights of the features. Afterwards, the system can recommend artworks according to personalized preferred similar styles.

關鍵字(中)

★ 畫作風格推薦
★ Sobel邊緣偵測
★ Canny edge邊緣偵測
★ 二維離散小波轉換
★ 人臉偵測

關鍵字(英)

論文目次

摘要 ................................ ................................ ................................ .............................. IV
Abstract ................................ ................................ ................................ ......................... V
致謝 ................................ ................................ ................................ .............................. VI
目錄 ................................ ................................ ................................ ............................ VII
圖目錄 ................................ ................................ ................................ .......................... IX
表目錄 ................................ ................................ ................................ .......................... XI
第一章緒論 ................................ ................................ ................................ .................. 1
1.1研究動機 ................................ ................................ ................................ .......... 1
1.2相關文獻 ................................ ................................ ................................ .......... 2
1.3系統使用流程 ................................ ................................ ................................ .. 3
1.4論文架構 ................................ ................................ ................................ .......... 6
第二章特徵方法回顧 ................................ ................................ ................................ .. 7
2.1 Sobel Operator ................................ ................................ ................................ . 7
2.2 Canny Edge Detection ................................ ................................ ..................... 9
2.2.1 Canny邊緣偵測準則邊緣偵測準則 ................................ ................................ ........... 9
2.2.2 Canny邊緣偵測演算法邊緣偵測演算法 ................................ ................................ ..... 11
2.3 離散小波轉換 ................................ ................................ ............................... 12
2.4 人臉偵測 ................................ ................................ ................................ ....... 15
2.4.1 AdaBoost ................................ ................................ ............................. 15
2.4.2哈爾特徵 ................................ ................................ ............................. 17
2.4.3級聯架構的分類器 ................................ ................................ ............. 19
2.5 膚色偵測 ................................ ................................ ................................ ....... 19
第三章畫作特徵選取和系統說明 ................................ ................................ ............ 21
VIII
3.1畫作的特徵 ................................ ................................ ................................ .... 21
3.1.1人臉特徵 ................................ ................................ ............................. 21
3.1.2膚色特徵 ................................ ................................ ............................. 23
3.1.3畫作色彩分布特徵 ................................ ................................ ............. 24
3.1.4 Sobel邊緣偵測的梯度強分佈特性 ................................ .............. 26
3.1.5 Canny邊緣特徵 ................................ ................................ ................. 31
3.1.6紋理的二維離散小波轉換特徵 ................................ ......................... 32
3.2畫作排序和比較特徵相似性方法 ................................ ................................ 34
3.2.1畫作排序 ................................ ................................ ............................. 34
3.2.2比較特徵相似性方法 ................................ ................................ ......... 36
3.3使用者的回饋資料 ................................ ................................ ........................ 36
3.4個人化模組 ................................ ................................ ................................ .... 37
3.5系統介面功能說明 ................................ ................................ ........................ 39
第四章實驗結果與分析 ................................ ................................ ............................ 42
4.1畫作資料庫收集 ................................ ................................ ............................ 42
4.2實驗結果與分析 ................................ ................................ ............................ 42
第五章結論與未來研究方向 ................................ ................................ .................... 47
參考文獻 ................................ ................................ ................................ ...................... 48

參考文獻

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指導教授

鄭旭詠

審核日期

2017-7-24

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