多尺度变换及其在图像纹理分类中的应用

《多尺度变换及其在图像纹理分类中的应用(英文)》在归纳分析国内外相关研究的基础上，从小波变换，轮廓变换，剪切波等多尺度变换，以及多尺度变换的子带选择等全新角度研究了图像纹理分类理论和方法，并且还对大数据图像纹理分析和分类问题进行了研究。主要内容包括：
（1）研究背景，对早期多尺度变换和图像纹理分类理论和方法给出一个概述性的总结。
（2）对当前主要多尺度变换的理论框架进行总结性介绍。
（3）研究小波域直方图比对的纹理分类理论和方法。
（4）研究轮廓波域泊松混合模型，及其基于该模型的纹理分类方法。
（5）研究基于轮廓波域聚类的纹理分类理论和方法。
（6）研究剪切波子带依赖性的线性回归模型，以及基于该模型的的纹理分类方法。
（7）研究轮廓波子带的统计特征提取方法，以及基于轮廓波域统计特征的纹理分类方法。
（8）研究了多尺度变换的子带选择理论，以及基于子带选择的图像纹理分类方法。
（9）针对当前视觉大数据分析的重要性和难题，研究了大数据图像纹理的分类理论和方法。

Preface
Chapter 1 Introduction 1
1.1 Multiscale Methods 1
1.2 Texture Databases 4
References 11
Chapter 2 Local Energy Histograms in Wavelet Domains for Texture Classification 13
2.1 Introduction 13
2.2 Proposed Texture Classification Method 14
2.3 Experimental Results 17
2.4 An Efficient Histogram-Based Texture Classification Method with Weighted Symmetrized Kullback-Leibler Divergence 22
2.5 Experimental Results 25
References 30
Chapter 3 Poisson Mixture Model in Contourlet Domains for Texture Classification 33
3.1 Introduction 33
3.2 Contourlet Transform 35
3.3 Poisson Mixtures and its BYY Harmony Learning 36
3.4 Proposed Bayesian Texture Classifier 38
3.5 Experimental Results 45
3.6 Conclusions 54
References 55
Chapter 4 Product Bernoulli Distributions in Contourlet Domains for Texture Classification 58
4.1 Introduction 58
4.2 Contourlet Transform 59
4.3 Proposed Texture Classification Method 60
4.4 Experimental Results 62
4.5 Statistical Contourlet Subband Characterization for Texture Image Retrieval 67
References 73
Chapter 5 Subband Clutering in Contourlet Domains for Texture Classification 76
5.1 Introduction 76
5.2 Contourlet Transform 77
5.3 New Texture Classification Method 78
5.4 Experimental Results 84
5.5 Conclusions 91
References 92
Chapter 6 Linear Regression Model in Shearlet Domains for Texture Classification and Retrieval 95
6.1 Introduction 95
6.2 Shearlet Transform 97
6.3 Texture Classification Based on Linear Regression Modeling the Dependence Between Shearlet Subbands 98
6.4 Texture Retrieval Based on Linear Regression Modeling 107
6.5 Experimental Results 111
6.6 Conclusions 118
References 118
Chapter 7 Heterogeneous and Incrementally Generated Histogram in Wavelet Domains for Texture Classification 122
7.1 Introduction 122
7.2 Related Work 124
7.3 Nonnegative Multiresolution Representation of Texture 125
7.4 Hessian Regularized Discriminative Nonnegative Matrix Factorization 129
7.5 NMV-based Texture Classification via HNMF 134
7.6 Experimental Results 135
7.7 Conclusions 144
References 145
Chapter 8 Multiscale Sampling in Wavelet Domains for Texture Classification 150
8.1 Introduction 150
8.2 Multiscale Rotation-invariant Texture Representation Framework 151
8.3 Experiments 155
8.4 Conclusions 159
References 160
Chapter 9 Multi-scale Counting and Difference Representation for Texture Classification 163
9.1 Introduction 163
9.2 Proposed Multi-scale Counting and Difference Representation of Texture Images 166
9.3 Experiments 172
9.4 Conclusions 179
References 180
Chapter 10 Jumping and Refined Local Pattern for Texture Classification 183
10.1 Introduction 183
10.2 Related Work 185
10.3 Jumping and Refined Local Pattern 186
10.4 Experimental Results 195
10.5 Conclusions 202
References 203
Chapter 11 Locally Directional and Extremal Pattern for Texture Classification 207
11.1 Introduction 207
11.2 Related Work 209
11.3 Locally Directional and Extremal Pattern 210
11.4 Experimental Results 217
11.5 Conclusions 225
References 225
Chapter 12 Multiscale Symmetric Dense Micro-block Difference for Texture Classification 230
12.1 Related Work 232
12.2 Texture Classication 234
12.3 Experiments 242
12.4 Conclusions 249
References 249
Chapter 13 Completed Extremely Nonnegative DMD for Color Texture Classification 255
13.1 Introduction 255
13.2 Related Work 258
13.3 Our Proposed Completed Extremely Nonnegative DMD Color Texture Representation Method 259
13.4 Experimental Results 267
13.5 Conclusions 277
References 278
Chapter 14 Compact Interchannel Sampling Difference Descriptor for Color Texture Classification 283
14.1 Background 283
14.2 The Proposed Compact Interchannel Sampling Difference Descriptor 286
14.3 Experimental Results 295
14.4 Conclusions 307
References 307
Chapter 15 Conclusions and Future Work 313
15.1 Conclusions 313
15.2 Future Work 315
Colourful Figures