臺灣博碩士論文加值系統

這篇論文對各種紋理特徵的擷取方法所獲得的紋理特徵，針對其特性在支援向量機和自我組織映射圖上做效能比較。特徵的擷取方法使用Gabor轉換和四種不同基底的小波轉換，並在支援向量機和自我組織映射圖上分析其特性，更甚者，從不同的角度來看，特徵值經過正規化後，在不同的分類器上會對其特性有何影響。
實驗使用的資料庫是從Brodatz資料庫中挑選盡可能是有一般規律性的九十六類，每一類為十六張，實驗中每一類的一半作為分類器學習之用，另一半則為測試之用，並將其所有效能測試結果列於第五章的實驗結果之中。

Experimental results of using various texture features based on Support Vector Machine (SVM) and Self-Organizing Map (SOM) are reported in this thesis. For classification, the texture features are derived from Gabor and four wavelet transforms (9/7, 5/3, Daubechies’ four, and Haar transforms). Then, the performance of various texture features will be evaluated by SVM and SOM. Moreover, a comparison of SVM and SOM for texture classification will be presented and illustrated in the final experimental results. Besides, the texture features for SVM and SOM have worked from a lightly different viewpoint; the training data with scaling or non-scaling process may heavily affect the classification rate.
Our database consists of 96 classes as homogeneous as possible (1536 images of size 128×128) from Brodatz’s album. So the performance evaluation of various texture features with SVM and SOM will be tested on our database and be reported in the experimental results.

1 Introduction 1
2 Support vector Machines 3
2.1 The Maximal Margin Classifier . . . . . . . . . . 3
2.2 The Feature Transformation of Kernel SVM . . . . . 8
2.3 Sequential Minimal Optimization (SMO) . . . . . . 11
2.3.1 Optimization for Two Lagrange Multipliers . . . 11
2.3.2 Heuristics for Choosing the Two Lagrange Multipliers . . . . . . . . . . . . . . . . . . . . . 15
2.4 Multi-class Support Vector Machines . . . . . . . 17
3 Self-Organizing Map 18
3.1 Algorithm for Kohonen’s Self-Organizing Map . . 18
3.2 Batch Training Algorithm for SOM . . . . . . . . . 22
3.3 Efficient Initialization Schemes for SOM . . . . . 23
4 Review of Texture Feature Extraction 25
4.1 Features Derived from Gabor Transform . . . . . . . 25
4.2 Features Derived from Wavelet . . . . . . . . . . . 26
4.2.1 Haar Wavelet Transform . . . . . . . . . . . . . 28
4.2.2 Daub4 Wavelet Transform . . . . . . . . . . . . . 28
4.2.3 Reversible 5/3 Wavelet Transform . . . . . . . . . 29
4.2.4 Irreversible 9/7 Wavelet Transform . . . . . . . . 30
5 Experimental Results 32
5.1 SVM-based classification using various kernels with scaled or non-scaled training data . . . . . . . . . . 32
5.2 SOM-based classification using different training iterations,different lattices and different neighborhoods . . . . . . . . . . . . . . . . . . . . . 33
5.3 SOM-based classification using different sizes of topology map . . . . . . . . . . . . . . . . . . . . . 34
5.4 The effect of SOM-based classification with scaled or
non-scaled training data . . . . . . . . . . . . . . . 35
5.5 Comparison of texture classification based on SVM and SOM . . . . . . . . . . . . . . . . . . . . . . . . . .36
6 Conclusion 37
References 38

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