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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16025完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 歐陽明 | |
| dc.contributor.author | Che-Hua Yeh | en |
| dc.contributor.author | 葉哲華 | zh_TW |
| dc.date.accessioned | 2021-06-07T17:58:29Z | - |
| dc.date.copyright | 2012-08-16 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-10 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16025 | - |
| dc.description.abstract | 在這個論文中，我們提出了一套個人化的照片評量系統。系統主要是跟據視覺上的美感自動對照片做評分和排序，除此之外，我們希望使用者可以自己定義何謂是美的照片。在使用上，我們希望是針對一般人所拍攝的照片作評量，而不是針對專業的攝影照片。
我們的系統會從照片中取出三種類別的特徵：構圖、顏色和光影、以及和個人喜好相關的特徵。之後利用RBF-ListNet演算法訓練出一個照片分數的預測模型，利用這個模型，我們可以預測照片的美感分數，進一步對照片做排序。為了讓使用者定義個人專屬的美感評量方式，我們提供三種使用者介面：Feature-based、Example-based、以及List-based方法。 在系統效果方面，我們的系統可以達到0.434的Kendall’s Tau值(排序關聯係數)，二元分類的準確率可以達到93%。我們也針對三種使用者介面做了使用者研究，結果顯示我們提出的三種介面都可以達到不錯的使用者經驗，尤其以example-based的效果最好。 | zh_TW |
| dc.description.abstract | In this dissertation, we propose a novel personalized ranking system for amateur photographs. Our goal of automatically ranking photographs is not intended for award-wining professional photographs but for photographs taken
by amateurs, especially when individual preference is taken into account. Photographs are described using 20 image features which can be categorized into three types: photo composition, color and intensity distribution, and features for personal preferences. We adopt RBF-ListNet as the ranking algorithm. RBF-ListNet is based on an efficient algorithm, ListNet, using radial basis functions. The performance of our system is evaluated in terms of Kendall’s tau rank correlation coefficient, precision-recall diagram, and binary classification accuracy. The Kendall’s tau value (0.434) is higher than those obtained by ListNet and support vector regression (SVR). The precision-recall diagram and binary classification accuracy (93%) is close to the best results to date for both overall system and individual features. To realize personalization in ranking, we propose three approaches: feature-based, example-based, and list-based approach. User studies indicate that all three approaches are effective in both aesthetic and personalized ranking. In particular, the example-based approach obtained the highest user experience rating among all three. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T17:58:29Z (GMT). No. of bitstreams: 1 ntu-101-D96944005-1.pdf: 12076185 bytes, checksum: c5b149f2787b4a341a3ddbfdf44a3b2e (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 致謝i
中文摘要iii Abstract v 1 Introduction 1 1.1 Computational Aesthetic . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Photograph Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Personalization in Multimedia . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Our Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Related Work 7 2.1 Computational Aesthetics . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Aesthetic Assessment in Photographs . . . . . . . . . . . . . . . 7 2.1.2 View Recommendation . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.3 Aesthetic Enhancement . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.1 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 Learning to Rank . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Personalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3 System Overview 15 4 Rules of Aesthetics 19 4.1 Photographic Composition . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.1.1 Rule of Thirds . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.1.2 Simplicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.1.3 Line Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2 Color and Intensity Distribution . . . . . . . . . . . . . . . . . . . . . . 25 4.2.1 Texture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.2.2 Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.2.3 Color Harmony . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.4 Intensity Balance . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.5 Contrast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.3 Personalized features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.3.1 Color preference . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.3.2 Black-and-white ratio . . . . . . . . . . . . . . . . . . . . . . . . 36 4.3.3 Portrait with face detection . . . . . . . . . . . . . . . . . . . . . 36 4.3.4 Aspect Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 5 Aesthetic Learning and Personalization 39 5.1 Learning to Rank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.2 ListNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.2.1 Personalization based on ListNet . . . . . . . . . . . . . . . . . . 40 5.3 RBF-ListNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.3.1 Personalization based on RBF-ListNet . . . . . . . . . . . . . . . 45 6 Experiments and User Study 49 6.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 6.2 Ranking Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 6.3 Ranking Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . 55 6.4 Binary Classification Accuracy . . . . . . . . . . . . . . . . . . . . . . . 58 6.5 Examples of Personalized Results . . . . . . . . . . . . . . . . . . . . . 60 6.6 User Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 7 Conclusions 69 7.1 Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Bibliography 71 | |
| dc.language.iso | en | |
| dc.subject | 個人化照片排序 | zh_TW |
| dc.subject | 照片排序 | zh_TW |
| dc.subject | 使用者介面 | zh_TW |
| dc.subject | 照片美學 | zh_TW |
| dc.subject | photo ranking | en |
| dc.subject | aesthetic analysis | en |
| dc.subject | photo selection | en |
| dc.subject | personalization | en |
| dc.subject | photo assessment | en |
| dc.title | 基於使用者回饋之個人化照片排序系統 | zh_TW |
| dc.title | Personalized Photograph Ranking and Selection System Considering Positive and Negative User Feedbacks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 廖弘源,傅楸善,陳煥宗,徐宏民,林奕成 | |
| dc.subject.keyword | 照片排序,照片美學,個人化照片排序,使用者介面, | zh_TW |
| dc.subject.keyword | photo ranking,photo assessment,personalization,photo selection,aesthetic analysis, | en |
| dc.relation.page | 86 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2012-08-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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