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Author: 吳孟倫
Meng-Luen Wu
Thesis Title: 應用樹狀分類器與類神經網路於影像構圖與調性風格美學評價的當代專業攝影指引之研究
On Professional Contemporary Style Photographing Instruction Based on Neural Tree Based Classifiers Applied to Image Aesthetics Assessment
Advisor: 范欽雄
Chin-Shyurng Fahn
Committee: 陳祝嵩
Chu-Song Chen
施仁忠
Zen-Chung Shih
李同益
Tong-Yee Lee
王榮華
Jung-Hua Wang
馮輝文
Huei-Wen Ferng
謝仁偉
Jen-Wei Hsieh
范欽雄
Chin-Shyurng Fahn
Degree: 博士
Doctor
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2017
Graduation Academic Year: 105
Language: 英文
Pages: 155
Keywords (in Chinese): 計量審美學資料探勘決策樹隨機森林類神經網路
Keywords (in other languages): Computational aesthetics, data mining, decision tree, random forest, artificial neural networks
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  • 本篇論文研究如何使用機器學習等人工智慧技術,讓電腦習得人類對於美感的抽象概念,並以此建構出一個攝影指引系統,教導相機使用者拍攝出符合專業攝影師水準之照片。研究中抓取網際網路社群上近年來較受歡迎之專業照片,導入資料探勘演算法,以分析當代之美學標準。本研究對於美感分析,分為兩個方面,其一為影像調性分析,其二為影像構圖分析。影像調性方面,針對影像的色彩、亮度、對比以及材質方面等作分析,判斷是否符合當代美學標準;影像構圖方面,針對影像的結構,判斷是否符合專業攝影師常見的構圖種類。
    所提出的攝影指引系統由樹狀分類器以及類神經網路組成,以隨機森林神經網路預測所輸入之影像是否符合當代美學標準,若不符合則分析決策路徑,自動給予少許修正建議,令使用者輕易將所輸入之照片修正為高水準照片。給予建議之決策樹以二元決策樹為主。決策樹相較於神經網路,其決策過程能夠以語意解釋,然而其資料分割為軸對齊,對於資料分類的準確度有所限制,本論文中將神經網路整合於決策樹中,再將決策樹改為隨機決策森林,兼顧神經網路與決策樹之優點,大幅提升其分類準確性。研究中亦探討無法給予指引的限制為何。攝影指引的方式分為影像調性以及影像構圖兩種:在影像調性方面,以語意方式提示受測影像中何種特徵需要增強或減弱;在影像構圖方面,則繪製方塊於輸入影像上給予提示,增強或減弱部分區塊的特徵。
    實驗中,我們預測一張影像是否受到社群網路歡迎,若單獨使用本論文所提出的影像調性或影像構圖特徵,可以達到85%以上的準確率,若將兩者結合使用,則可達到91%以上的準確率。使用隨機森林與神經網路結合作為分類器時,所得的準確率最高。此外,所提出的方法亦在影像調性以及影像構圖兩個方面,提出了有效的攝影指引,使影像調性更為和諧、構圖更為平衡,主體更為凸顯。


    In this dissertation, we study on how to use artificial intelligence and data mining technologies to make computers able to perceive the concept of beauty, which is an abstract idea, and design a photographing instruction system accordingly. We collect contemporary style images captured in recent years on social networks for analysis. In our instruction system, there are two parts of instruction, one is image characteristics, and the other is image composition. The image characteristics refers to the color and textures, while the image composition refers to the structure of an image.
    Our proposed photographing instructor is composed of tree-based classifiers and artificial neural networks, and form a random forest to predict whether an image meets the criterions of the contemporary style. Binary decision tree are built for photographing instruction. However, the decision tree suffers from axis-aligned problem, which limits its accuracy. Therefore, we combine the decision tree and neural network, and use the subsets to build multiple random trees as random forest to improve the accuracy. We also described about the limitations of the instruction system. The system gives semantic sentences to users for image characteristics enhancement, and use blocks to indicate which regions should be improved for image composition.
    In the experiments, we predict whether an image is favorable. When using image characteristics and composition features separately, and achieved 85% accuracy. When combining the two types of features, the accuracy was above 91%. In addition, the proposed instruction system is able to give correct suggestions. After applying the suggestions from our proposed system, the colors were more harmonized, the compositions were more balanced, and the main subjects were enhanced.

    Contents 指導教授推薦書 i 口試委員審定書 ii 中文摘要 iii Abstract iv 致謝 v Contents vi List of Figures x List of Tables xix Chapter 1 Introduction 1 1.1 Overview 1 1.1.1 Image Characteristics 3 1.1.2 Image Composition 3 1.2 Motivation 4 1.3 Challenges of Image Aesthetics 5 1.4 The Aims and Goals of the Dissertation 7 1.5 System Description 9 1.6 Dissertation Organization 12 Chapter 2 Literature Review 13 2.1 Assessment by Image Characteristics 13 2.2 Assessment by Tags 14 2.3 Photo Ranking Systems 16 2.4 Interestingness 17 2.5 Image Aesthetical Composition 19 2.5.1 Image Aesthetical Composition Classification 19 2.5.2 Image Aesthetical Composition Optimization 21 2.6 Robotic Photographer 22 2.7 Convolution Neural Networks 24 2.8 Automatic Photographing Instructions 27 Chapter 3 Image Characteristics Features 28 3.1 Color Space Conversion 28 3.1.1 sRGB to CIEXYZ 30 3.1.2 RGB to sRGB 30 3.1.3 CIEXYZ to CIELab 31 3.2 Color Components 31 3.2.1 Average Color Extraction 33 3.2.2 Representative Color Extraction 34 3.2.3 Degree of Achromatic 35 3.3 Degree of Brightness 37 3.4 Degree of Contrast 39 3.4.1 Achromatic Contrast 40 3.4.2 Color Contrast 41 3.5 Saturation 41 3.6 Sharpness 42 3.6.1 Maximum Sharpness 43 3.6.2 Sharpness Blur Ratio 45 3.7 Overexposure and Underexposure 47 3.8 Face Detection 49 3.8.1 Haar-like features 49 3.8.2 AdaBoost 50 3.9 Feature Analysis 53 3.9.1 Histogram Analysis 53 3.9.2 Feature Selection 56 3.10 Score Prediction Using Characteristic Features 57 3.11 Summary 60 Chapter 4 Image Composition 61 4.1 Importance of Image Aesthetical Composition 61 4.2 Common Types of Image Composition 62 4.2.1 Central Composition 62 4.2.2 Diagonal Composition 62 4.2.3 Horizontal Composition 63 4.2.4 Perspective Composition 63 4.2.5 Symmetrical Composition 63 4.2.6 Rule-of-thirds Composition 64 4.2.7 Vertical Composition 64 4.3 Saliency Map Generation 65 4.4 Prominent Lines 66 4.5 Composition Recognition Using Defined Rules 67 4.5.1 The Ratio of Horizontal Lines 68 4.5.2 The Ratio of Vertical Lines 69 4.5.3 The Ratio of Diagonal Lines 70 4.5.4 The Ratio of Diagonal Lines 70 4.6 Salient Region Based Rules 70 4.6.1 Nearest Distance between the Center Point of the Photo and the Centroid of the Maximum Saliency Region 71 4.6.2 Nearest Distance between Each of Power Point of the Photo and the Centroid of the Saliency Map 71 4.6.3 Feature Matching for Symmetry Detection 72 4.6.4 Local Area Relation Rules 72 4.6.5 Horizontal Linearity 73 4.6.6 Vertical Linearity 74 4.7 Image Pyramid Analysis 74 4.8 Image Composition Classification 77 4.9 Image Score Prediction Using Composition 84 4.9.1 Histogram Analysis 84 4.9.2 Prediction Using Compositions as Components 85 4.10 Summary 87 Chapter 5 Aesthetics Score Prediction 89 5.1 Data Preprocessing 89 5.1.1 Feature Selection 89 5.1.2 Feature Extraction 91 5.2 Clustering 92 5.3 Multilayer Perceptron 94 5.4 Decision Trees 98 5.5 Neural Decision Trees 101 5.5.1 Shortcoming of Decision Trees 101 5.5.2 Principal Component Analysis for Decision Trees 102 5.5.3 Support Vector Machines in Decision Tree 103 5.5.4 Artificial Neural Networks in Decision Tree 103 5.6 Random Forest 104 5.6.1 Neural Networks in Random Forest 106 5.7 Summary 106 Chapter 6 Photographing Instructions 107 6.1 Basic Instruction Mechanism 110 6.2 Limitations for Improvement Suggestion 111 6.3 Independency between Image Aesthetical Features 113 6.4 Possible Instructions 117 6.4.1 Gamma correction 119 6.4.2 Gaussian blur 120 6.4.3 Sharpness 121 6.4.4 Contrast 121 6.4.5 Other enhancement features 122 6.5 Approximation method 123 Chapter 7 Experimental Results 125 7.1 Crowd-sourcing from DPChallenge 125 7.2 Experimental Setup 129 7.3 Results in Image Aesthetics Score Prediction 131 7.4 Results in Image Composition Recognition 134 7.5 Results in Composition Suggestions 137 7.5.1 Image Style Suggestions 137 7.5.2 Image Composition Suggestions 140 Chapter 8 Conclusion and Future Works 143 8.1 Conclusion 143 8.2 Future Work 145 References 146

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