臺灣博碩士論文加值系統

此研究目標利用卷積類神經網路發展一個蠟染影像分類系統，此系統在此被稱為BICNN系統。由於蠟染是獨特多樣圖的紡織元素，傳統方法比較難取的獨特特徵與比對特徵演算法. 現今深度學習的卷積類神經網路可以被運用於設計蠟染影像分類器，由於其有非監督式自動尋找蠟染獨特特徵. 本研究中蠟染影像集建構是利用針對蠟染影像實施旋轉、位移、裁剪、放大、縮小，來獲得更大蠟染影像集. 一部份影像集當作BICNN系統訓練樣本、其餘部份樣本當作是測試BICNN系統延展能力，實驗結果顯示BICNN系統分類正確性效能可達到滿意結果，主要原因是本研究由非常大的參數集空間找到一個BICNN系統之最佳參數集。

This research aims to develop a batik image classification system using Convolutional Neural Networks (CNN). Hereafter, it is called the BICNN system. Because Batik is specific textile variously, it is difficult to devise Batik image classification by traditional approaches to getting unique features and to producing effective feature-matching algorithms. Nowadays, a deep learning approach is called CNN which can be employed in playing a role for the Batik-image classifier due to its powerful ability to automatically search for unique features of Batik images with unsupervised learning approaches. Here a Batik image dataset can be built by rotating, shifting, cropping, zooming in, and zooming out Batik images. A portion of the dataset is utilized in training the BICNN system and then reminder of the dataset is exploited in investigating the generalization of the BICNN system. Experimental results show that the accuracy performance of the BICNN system reaches satisfying results by finding out a best parameter set from lots of parameter sets of the BICNN system.

Abstract......i
摘要......ii
Acknowledgements......iii
Table of Contents......iv
List of Tables......vi
List of Figures......vii
Chapter 1 Introduction......1
1.1 Research Background......1
1.2 Research Motivation......3
1.3 Research Objective......4
1.4 Research Structure......4
1.5 Research Flowchart......5
Chapter 2 Background......7
2.1 Batik......7
2.2 Image Classification......8
2.3 Deep Learning......9
2.4 Convolutional Neural Network......10
2.5 Performance Evaluation Indices......12
2.5.1 Confusion Matrix (Binary Classification)......12
2.5.2 Confusion Matrix (Multiple Classification)......14
2.5.3 Receiver Operator Characteristic (ROC)......16
Chapter 3 Literature reviews for Batik Image Classification......18
Chapter 4 System Description......22
4.1 The structure of BICNN system......22
4.2 The CNN structure......24
4.3 Pooling......25
4.4 Activation......25
4.4.1 ReLU (Rectified Linear Unit) Activation Function......26
4.4.2 Sigmoid or Logistic Activation Function......26
4.4.3 Tanh or Hyperbolic Tangent Activation Function......27
4.5 Monte Carlo......28
4.6 Parameters for CNN Architecture......28
4.6.1 Number of Hidden Layers and Units......28
4.6.2 Dropout......28
4.6.3 Learning Rate......29
4.6.4 Momentum......29
4.6.5 Number of Epochs......29
4.6.6 Batch size......29
4.6.7 Convolutional Layer......30
Chapter 5 Experimental results......31
5.1 Batik Image Datasets and programs......31
5.2 Computer Simulations......33
5.2.1 The case with 10 classes with 128 filters......33
5.2.2 The case 30 classes with 128 filters......34
5.2.3 The case 50 classes with 128 filters......35
5.2.4 The case 10 classes with 256 filters......35
5.2.5 The case 30 classes with 256 filters......36
5.2.6 The case 50 classes with 256 filters......37
5.2.7 The case 10 classes with 512 filters......38
5.2.8 The case 30 classes with 512 filters......39
5.2.9 The case 50 classes with 512 filters......39
5.2.10 The Confusion Matrix for 10 classes......44
5.2.11 The Receiver Operating Characteristic Curve for 10 classes......45 5.2.12 The best results of three kinds of classes (10, 30, and 50 classes)......46
5.2.13 Learning curve......47
5.2.14 The example of batik image classification......49
Chapter 6 Conclusions and Future Works......51
6.1 Conclusions......51
6.2 Future Works......53
References......54

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