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研究生:趙思涵
研究生(外文):Sz-Han Chao
論文名稱:基於風格辨識之服裝推薦系統
論文名稱(外文):Clothes Recommendation System based on Style Recognition
指導教授:李明穗
口試委員:楊佳玲周承復
口試日期:2016-07-14
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:62
中文關鍵詞:服裝推薦風格辨識服裝參數色票構面情緒模型
外文關鍵詞:clothes recommendationstyle recognitionclothing attributecolor themedimension emotion model
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很多人每天都會花上許多時間選擇當天要穿著的服裝,對於他們來說,花許多時間選擇每天的穿搭是一向不小的困擾,為了幫助這些人更容易地選擇服裝,我們設計了一套依據風格的服裝推薦系統,可以推薦適當的穿搭。在我們的系統中,將衣服的風格定義為一種情緒,代表人們在看見衣服或是看見某個人穿著這件衣服之後的感覺,因此我們利用構面情緒模型(dimension emotion model)來設計我們的風格空間(style space)。除了使用低階特徵之外,我們使用服裝參數(clothing attribute)作為連接低階特徵與高階服裝風格的中階特徵,並利用支持向量回歸(support vector regression)來訓練風格分數函數(style score function)用於辨識服裝風格。我們使用風格色票(style color theme)來評估穿搭服裝的配色。因為沒有適合此系統的資料庫,我們建立了兩個新的資料庫,並使用我們的風格空間來標示其風格。經過實驗與使用者評估,以多數人的感官來說,此系統的推薦結果大部分都符合指定風格(情緒)。

Many people have trouble selecting clothes in the everyday morning. They often cost much time to choose the clothes. In order to help them easier choose clothes, we proposed a clothes recommendation system based on the style to recommend the suitable clothes for them. In our system, we define the style of clothes as the emotion. It means the feeling when people look at the clothes or look at a person wearing clothes. Thus, we use the dimension emotion model to design the style space. In addition to low-level features, the clothing attributes are used as the mid-level features. Then we train style score functions by the standard support vector regression to recognize the styles of clothes. Besides, the style color theme is used to assess the color matching of clothes. Because there is not suitable dataset for our system, we construct two new datasets with style labels by our style space. The experiments and user study show that the clothes recommended by our system almost match the style (emotion) in common sense.

口試委員會審定書 i
誌謝 ii
中文摘要 iii
Abstract iv
Contents v
List of Figures vii
Chapter 1 Introduction 1
Chapter 2 Related Works 3
2.1 Emotion Research 3
2.2 Clothing Research 5
Chapter 3 Dataset 8
3.1 The GI Dataset 8
3.2 The CI Dataset 9
Chapter 4 Proposed Method 14
4.1 Pre-processing 16
4.2 Feature Extraction 17
4.3 Models 24
4.3.1 Upper-lower Classifier 24
4.3.2 Clothing Attribute Model 25
4.3.3 Style Score Function 26
4.3.4 Style Color Theme 28
Chapter 5 Experiment Results 33
5.1 Results and Accuracy 33
5.1.1 Upper-lower Classifier 33
5.1.2 Clothing Attribute Model 33
5.1.3 Style Score Function 35
5.1.4 Style Color Theme 42
5.2 User Study 44
5.3 Compare Two Style Score Functions 46
Chapter 6 Application 47
6.1 Scenario 1: Topic 48
6.2 Scenario 2: Topic and Shop Recommendation 50
6.3 Scenario 3: Assign Clothes 52
6.4 Scenario 4: Assign Clothes and Shop Recommendation 53
6.5 Scenario 5: Random 55
Chapter 7 Conclusion 57
7.1 Summary 57
7.2 Future Work 58
Reference 59


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