Fundamentals of image data mining: analysis, features, classification and retrieval
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2021]
|
| Ausgabe: | Second edition |
| Schriftenreihe: | Texts in computer science
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxxiii, 363 Seiten Illustrationen, Diagramme (teilweise farbig) |
| ISBN: | 9783030692506 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV047646014 | ||
| 003 | DE-604 | ||
| 005 | 20230228 | ||
| 007 | t | ||
| 008 | 211217s2021 a||| b||| 00||| eng d | ||
| 020 | |a 9783030692506 |9 978-3-030-69250-6 | ||
| 035 | |a (OCoLC)1294760597 | ||
| 035 | |a (DE-599)BVBBV047646014 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-11 |a DE-355 | ||
| 084 | |a ST 330 |0 (DE-625)143663: |2 rvk | ||
| 084 | |a ST 530 |0 (DE-625)143679: |2 rvk | ||
| 084 | |a DAT 600f |2 stub | ||
| 084 | |a DAT 760f |2 stub | ||
| 100 | 1 | |a Zhang, Dengsheng |e Verfasser |0 (DE-588)1238716180 |4 aut | |
| 245 | 1 | 0 | |a Fundamentals of image data mining |b analysis, features, classification and retrieval |c Dengsheng Zhang |
| 250 | |a Second edition | ||
| 264 | 1 | |a Cham, Switzerland |b Springer |c [2021] | |
| 264 | 4 | |c © 2021 | |
| 300 | |a xxxiii, 363 Seiten |b Illustrationen, Diagramme (teilweise farbig) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Texts in computer science | |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Informatik |0 (DE-588)4026894-9 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Multimedia data mining | |
| 689 | 0 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | 1 | |a Informatik |0 (DE-588)4026894-9 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-030-69251-3 |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033030147&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-033030147 | ||
Datensatz im Suchindex
| _version_ | 1804183106886828032 |
|---|---|
| adam_text | Contents Part I Preliminaries 1 Fourier Transform.................................................................................... 1.1 Introduction....................................................................................... 1.2 Fourier Series.................................................................................... 1.2.1 Sinusoids.............................................................................. 1.2.2 Fourier Series....................................................................... 1.2.3 Complex Fourier Series..................................................... 1.3 Fourier Transform.............................................................................. 1.4 Discrete Fourier Transform............................................................. 1.4.1 DFT............................. 1.4.2 Uncertainty Principle......................................................... 1.4.3 Nyquist Theorem................................................................ 1.5 2D Fourier Transform....................................................................... 1.6 Properties of 2D Fourier Transform................................................ 1.6.1 Separability......................................................................... 1.6.2 Translation............................................................................ 1.6.3 Rotation................................................................................ 1.6.4 Scaling................................................................................ 1.6.5 Convolution
Theorem......................................................... 1.7 Techniques of Computing FTSpectrum.......................................... 1.8 Summary............................................................................................ 1.9 Exercises........................................................................................... References.................................................................................................... З З 4 4 6 8 9 10 10 11 13 14 15 15 15 15 18 18 19 21 22 22 2 Windowed Fourier Transform................................................................ 2.1 Introduction....................................................................................... 2.2 Short-Time Fourier Transform......................................................... 2.2.1 Spectrogram......................................................................... 2.3 Gabor Filters.......................................... 2.3.1 Gabor Transform............................................................... 2.3.2 Design of GaborFilters..................................................... 2.3.3 Spectra of GaborFilters..................................................... 23 23 24 25 26 26 27 30 xiii
Contents xiv 3 2.4 Discrete Cosine Transform............................................................. 2.4.1 İD DCT Model.................................................................. 2.4.2 DCT Bases......................................................................... 2.4.3 2D DCT.............................................................................. 2.4.4 Computation of 2D DCT................................................... 2.5 Summary............................................................................................ 2.6 Exercises............................................................................................ References................ . .................... 31 31 33 35 35 40 41 43 Wavelet Transform................ ............................................................... 3.1 Discrete Wavelet Transform........................................................... 3.2 Multiresolution Analysis.................................................................. 3.3 Fast Wavelet Transform.................................................................. 3.3.1 DTW Decomposition Tree...................................... 3.3.2 ID Haar Wavelet Transform............................................. 3.3.3 2D Haar Wavelet Transform............................................. 3.3.4 Application of DWT on Image........................................ 3.4 Summary............................................................................................ 3.5
Exercises............................................................................................ 45 45 46 48 48 50 51 53 54 54 Part II 4 Image Representation and Feature Extraction Color Feature Extraction ......................................................................... 4.1 Introduction....................................................................................... 4.2 Color Space....................................................................................... 4.2.1 CIE XYZ, xyY Color Spaces............................................. 4.2.2 RGB Color Space .............................................................. 4.2.3 HSV, HSL and HSI Color Spaces................................... 4.2.4 CIE LUV Color Space....................................................... 4.2.5 Y CbCr Color Space........................................ 4.3 Image Clustering and Segmentation............................................... 4.3.1 X-means Clustering............................................................ 4.3.2 JSEG Segmentation............................................................ 4.4 Color Feature Extraction .................................................................. 4.4.1 Color Histogram................................................................... 4.4.1.1 Component Histogram...................................... 4.4.1.2 Indexed Color Histogram............................... 4.4.1.3 Dominant Color Histogram............................ 4.4.2 Color Structure Descriptor................................................ 4.4.3 Dominant
Color Descriptor................................................ 4.4.4 Color Coherence Vector..................................................... 4.4.5 Color Correlogram.............................................................. 4.4.6 Color Layout Descriptor..................................................... 59 59 60 60 64 66 71 73 74 74 75 77 77 78 79 79 80 82 84 85 87
Contents 5 6 XV 4.4.7 Scalable Color Descriptor................................................. 4.4.8 Color Moments................................................................. 4.5 Image Enhancement........................................................................ 4.5.1 Noise Removal.................................................................... 4.5.2 Contrast Enhancement...................................................... 4.6 Summary................................................................... 4.7 Exercises..................... i................................................................... References.................................................................................................... 87 88 90 90 90 96 97 99 Texture Feature Extraction...................................................................... 5.1 Introduction...................................................................................... 5.2 Spatial Texture Feature Extraction Methods................................. 5.2.1 Tamura Textures............................................................... 5.2.2 Gray-Level Co-Occurrence Matrices............................... 5.2.3 Markov Random Field...................................................... 5.2.4 Fractal Dimension..................................................... 5.2.5 Discussions......................................................................... 5.3 Spectral Texture Feature Extraction Methods............................... 5.3.1 DCT-Based Texture
Feature............................................. 5.3.2 Texture Features Based on Gabor Filters........................ 5.3.2.1 Gabor Filters .................................................... 5.3.2.2 Gabor Spectrum............................................... 5.3.2.3 Texture Representation.................................... 5.3.2.4 Rotation Invariant Gabor Features................. 5.3.3 Texture Features Based on WaveletTransform................ 5.3.3.1 Selection and Application of Wavelets.......... 5.3.3.2 Contrast of DWT and Other Spectral Transforms........................................................ 5.3.3.3 Multiresolution Analysis.................................. 5.3.4 Texture Features Based on CurveletTransform.............. 5.3.4.1 Curvelet Transform........................................... 5.3.4.2 Discrete CurveletTransform........................... 5.3.4.3 Curvelet Spectra............................................... 5.3.4.4 Curvelet Features............................................. 5.3.5 Discussions........................................................................ 5.4 Summary............................................................................................ 5.5 Exercises............................................................................................ References.................................................................................................... 101 101 102 102 105 106 107 108 109 109 110 110 112 112 114 117 117 Shape
Representation............................................................................... 6.1 Introduction........................................... 6.2 Perceptual Shape Descriptors........................................................... 6.2.1 Circularity and Compactness............................................ 6.2.2 Eccentricity and Elongation............................................... 133 133 134 134 135 121 121 122 122 125 129 129 130 130 131 131
xvi Contents 6.2.3 Convexity and Solidarity.................................................. 6.2.4 Euler Number..................................................................... 6.2.5 Bending Energy.................................................................. 6.3 Contour-Based Shape Methods...................................................... 6.3.1 Shape Signatures......................................... 6.3.1.1 Position Function.............................................. 6.3.1.2 Centroid Distance.............................................. 6.3.1.3 . Angular Functions........................................ 6.3.1.4 Curvature Signature .......................................... 6.3.1.5 Area Function..................................................... 6.3.1.6 Discussions.......................................................... 6.3.2 Shape Context..................................................................... 6.3.3 Boundary Moments................................................... 6.3.4 Stochastic Method................ .. .......................................... 6.3.5 Scale Space Method........................................................... 6.3.5.1 Scale Space....................................................... 6.3.5.2 Curvature Scale Space....................................... 6.3.6 Fourier Descriptor............................................................. 6.3.7 Discussions......................................................................... 6.3.8 Syntactic Analysis..............................................................
6.3.9 Polygon Decomposition.................................................... 6.3.9.1 Chain Code Representation.............................. 6.3.9.2 Smooth Curve Decomposition......................... 6.3.9.3 Discussions.......................................................... 6.4 Region-Based Shape Feature Extraction........................................ 6.4.1 Geometric Moments........................................................... 6.4.2 Complex Moments.............................................................. 6.4.3 Generic Fourier Descriptor............................................ . 6.4.4 Shape Matrix.................................................... 6.4.5 Shape Profiles..................................................................... 6.4.5.1 Shape Projections.............................................. 6.4.5.2 Radon Transform.............................................. 6.4.6 Discussions ......................................................................... 6.4.7 Convex Hull....................................................................... 6.4.8 Medial Axis................................... 6.5 Summary............................................................................................. 6.6 Exercises............................................................................................. References..................................................................................................... 136 137 138 139 139 139 140 141 142 145 146 146 148 148 149 149 149 152 152 153 154 156 156 157 157 158 159 162 165 166 166 167
170 171 172 173 174 175
xvii Contents Part Ш Image Classification and Annotation 7 Bayesian Classification............................................................................. 7.1 Introduction......................................................... 7.2 Naïve Bayesian Image Classification............................................. 7.2.1 NB Formulation................................................................. 7.2.2 NB with IndependentFeatures.......................................... 7.2.3 NB with Bag of Features.................................................. 7.3 Image Annotation with Word Co-occurrence............................... 7.4 Image Annotation with Joint Probability...................................... 7.5 Cross-Media Relevance Model...................................................... 7.6 Image Annotation with Parametric Model...................................... 7.7 Image Classification with Gaussian Process............................. 7.8 Summary........................................................................................... 7.9 Exercises........................................................................................... References.................................................................................................... 183 183 185 185 188 188 188 191 193 194 196 198 199 200 8 Support Vector Machine........................................................................... 8.1 Linear Classifier................................................................................ 8.1.1 A Theoretical
Solution...................................................... 8.1.2 An Optimal Solution......................................................... 8.1.3 A Suboptimal Solution...................................................... 8.2 К Nearest Neighbor Classification.................................................. 8.3 Support Vector Machine.................................................................. 8.3.1 The Perceptron.................................................................... 8.3.2 SVM—The Primal Form.................................................. 8.3.2.1 The Margin Between Two Classes................. 8.3.2.2 Margin Maximization..................................... 8.3.2.3 The Primal Form of SVM............................... 8.3.3 The Dual Form of SVM.................................................... 8.3.3.1 The Dual-Form Perceptron............................. 8.3.4 Kernel-Based SVM ........................................................... 8.3.4.1 The Dual-Form SVM Versus NN Classifier.......................................................... 8.3.4.2 Kernel Definition.............................................. 8.3.4.3 Building New Kernels....................................... 8.3.4.4 The Kernel Trick............................................. 8.3.5 The Pyramid Match Kernel............................................... 8.3.6 Discussions......................................................................... 8.4 Fusion of SVMs................................................................................ 8.4.1 Fusion of Binary SVMs
.................................................... 8.4.2 Multilevel Fusion of SVMs............................................... 8.4.3 Fusion of SVMs with Different Features........................ 8.5 Summary............................................................................................ 201 201 202 203 204 205 206 207 208 208 210 211 211 213 213 213 215 217 218 220 223 223 223 224 224 226
xviii 9 Contents 8.6 Exercises................................................... References.................................................................................................... 226 228 Artificial Neural Network......................................................................... 9.1 Introduction...................................................................... 9.2 Artificial Neurons.............................................................................. 9.2.1 An AND Neuron................................................................ 9.2.2 An OR Neuron .................................................................. 9.3 Perceptron ......................................................................................... 9.4 Nonlinear Neural Network................................................................ 9.5 Activation and Inhibition.................................................................. 9.5.1 Sigmoid Activation.............................................................. 9.5.2 Shunting Inhibition.............................................................. 9.6 The Backçropagation Neural Network . ................................... . . . 9.6.1 The BP Network and ErrorFunction................................ 9.6.2 Layer К Weight Estimation and Updating..................... 9.6.3 Layer А-l Weight Estimation andUpdating.................. 9.6.4 The BP Algorithm ............................................................. 9.7 Convolutional Neural Network.......... ............................................ 9.7.1 CNN
Architecture ............................................................. 9.7.2 Input Layer........................................................................ 9.7.3 Convolution Layer 1 (Cl).................................................. 9.7.3.1 2D Convolution................................................ 9.7.3.2 Stride and Padding............................................ 9.7.3.3 Bias . ................................................................... 9.7.3.4 Volume Convolution in Layer Cl.................. 9.7.3.5Depth of the Feature Map Volume....................... 9.7.3.6 ReLU Activation.............................................. 9.7.3.7 Batch Normalization.......................................... 9.7.4 Pooling or Subsampling Layer 1 (SI).......... .............. 9.7.5 Convolution Layer 2 (C2)............................ 9.7.6 Hyperparameters ................................................................ 9.8 Implementation of CNN..................................................... 9.8.1 CNN Architecture............................................................. 9.8.2 Filters of the Convolution Layers.................................... 9.8.3 Filters of the Fully Connected Layers............................. 9.8.4 Feature Maps of Convolution Layers............................... 9.8.5 Matlab Implementation...................................................... 9.9 Summary............................................................................................ 9.10
Exercises............................................................................................ References.................................................................................................... 229 229 230 232 232 234 235 238 238 239 240 240 241 242 245 246 246 247 247 249 249 250 250 250 252 252 253 254 254 255 256 257 257 260 265 267 267 270
xix Contents 10 Image Annotation with Decision Tree................................................... 10.1 Introduction ...................................................................................... 10.2 ID3.................................................................................................... 10.2.1 ID3 Splitting Criterion..................................................... 10.3 C4.5............................................................ 10.3.1 C4.5 Splitting Criterion..................................................... 10.4 CART.............................. 10.4.1 Classification Tree Splitting Criterion............................ 10.4.2 Regression Tree Splitting Criterion................................. 10.4.3 Application of Regression Tree........................................ 10.5 DT for Image Classification............................................................. 10.5.1 Feature Discretization........................................................ 10.5.2 Building the DT................................................................. 10.5.3 Image Classification and Annotation with DT.............. 10.6 Summary........................................................................................... 10.7 Exercises........................................................................................... References.................................................................................................... Part IV 271 271 273 274 275 275 276 276 278 278 279 279 282 285 287 287 289 Image Retrieval and Presentation 11 Image
Indexing.......................................................................................... 11.1 Numerical Indexing.................. ................................................... . 11.1.1 List Indexing..................................................................... 11.1.2 Tree Indexing..................................................................... 11.2 Inverted File Indexing...................................................................... 11.2.1 Inverted File for TextualDocuments Indexing............... 11.2.2 Inverted File for Image Indexing..................................... 11.2.2.1 Determine the Area Weight aw..................... 11.2.2.2 Determine the Position Weight pw................ 11.2.2.3 Determine theRelationship Weight rw............ 11.2.2.4 Inverted Filefor Image Indexing....................... 11.3 Summary.......................................................................................... 11.4 Exercises......................................................................................... References................ ................................................................................... 293 293 293 294 295 295 297 297 298 298 299 300 300 301 12 Image Ranking........................................................................................... 12.1 Introduction . . . .............................................................................. 12.2 Similarity Measures....................................................................... 12.2.1 Distance
Metric................................................................. 12.2.2 Minkowski-Form Distance.............................................. 12.2.3 Mass-Based Distance.............. ...................................... 12.2.4 Cosine Distance................................................................. 12.2.5 χ2 Statistic.......................................................................... 12.2.6 Histogram Intersection..................................................... 303 303 304 304 304 305 309 310 310
Contents XX 13 12.2.7 Quadratic Distance............................................................ 12.2.8 Mahalanobis Distance....................................................... 12.3 Performance Measures.................................................................... 12.3.1 Recall and Precision Pair (RPP).................................... 12.3.2 F-measure.......................................................................... 12.3.3 Percentage of Weighted Hits (PWH).............................. 12.3.4 Percentage of Similarity Ranking (PSR)....................... 12.3.5 Bullseye Accuracy............................................................ 12.4 Hypothesis Testing....................................... 12.4.1 Introduction . ... .............................................................. 12.4.2 Fundamental Theorems of Statistics................................ 12.4.3 Properties of Normal Distribution.................................. 12.4.4 HT on a Single Population........................................ 12.4.5 Power of Test.............................. 12.4.6 HT on Difference of Means............................................. 12.4.7 Summary of HT.................................................................. 12.4.8 Margin of Error................................................................. 12.5 Summary............................................................................................ 12.6 Exercises............................................................................................
References.................................................................................................... 311 312 313 314 316 317 318 319 319 319 320 321 323 326 327 329 329 331 331 333 Image Presentation.................... 13.1 Introduction............................... 13.2 Caption Browsing.............................................................................. 13.3 Category Browsing............................................................................ 13.3.1 Category Browsing on the Web...................................... 13.3.2 Hierarchical Category Browsing...................................... 13.4 Content Browsing...................................... 13.4.1 Content Browsing in 3D Space....................................... 13.4.2 Content Browsing with Fish Eye View.......................... 13.4.3 Force-Directed Content Browsing................................... 13.5 Query by Example............................................................................ 13.6 Query by Keywords.......................................................................... 13.7 Summary............................... 13.8 Exercises............................................................................................ References..................................................................................................... 335 335 335 336 337 339 339 341 341 344 344 347 350 351 352 Appendix: Deriving the Conditional Probability of a Gaussian Process........................................................................... 353
Index..................................................................................................................... 359
|
| adam_txt |
Contents Part I Preliminaries 1 Fourier Transform. 1.1 Introduction. 1.2 Fourier Series. 1.2.1 Sinusoids. 1.2.2 Fourier Series. 1.2.3 Complex Fourier Series. 1.3 Fourier Transform. 1.4 Discrete Fourier Transform. 1.4.1 DFT. 1.4.2 Uncertainty Principle. 1.4.3 Nyquist Theorem. 1.5 2D Fourier Transform. 1.6 Properties of 2D Fourier Transform. 1.6.1 Separability. 1.6.2 Translation. 1.6.3 Rotation. 1.6.4 Scaling. 1.6.5 Convolution
Theorem. 1.7 Techniques of Computing FTSpectrum. 1.8 Summary. 1.9 Exercises. References. З З 4 4 6 8 9 10 10 11 13 14 15 15 15 15 18 18 19 21 22 22 2 Windowed Fourier Transform. 2.1 Introduction. 2.2 Short-Time Fourier Transform. 2.2.1 Spectrogram. 2.3 Gabor Filters. 2.3.1 Gabor Transform. 2.3.2 Design of GaborFilters. 2.3.3 Spectra of GaborFilters. 23 23 24 25 26 26 27 30 xiii
Contents xiv 3 2.4 Discrete Cosine Transform. 2.4.1 İD DCT Model. 2.4.2 DCT Bases. 2.4.3 2D DCT. 2.4.4 Computation of 2D DCT. 2.5 Summary. 2.6 Exercises. References. . . 31 31 33 35 35 40 41 43 Wavelet Transform. . 3.1 Discrete Wavelet Transform. 3.2 Multiresolution Analysis. 3.3 Fast Wavelet Transform. 3.3.1 DTW Decomposition Tree. 3.3.2 ID Haar Wavelet Transform. 3.3.3 2D Haar Wavelet Transform. 3.3.4 Application of DWT on Image. 3.4 Summary. 3.5
Exercises. 45 45 46 48 48 50 51 53 54 54 Part II 4 Image Representation and Feature Extraction Color Feature Extraction . 4.1 Introduction. 4.2 Color Space. 4.2.1 CIE XYZ, xyY Color Spaces. 4.2.2 RGB Color Space . 4.2.3 HSV, HSL and HSI Color Spaces. 4.2.4 CIE LUV Color Space. 4.2.5 Y'CbCr Color Space. 4.3 Image Clustering and Segmentation. 4.3.1 X-means Clustering. 4.3.2 JSEG Segmentation. 4.4 Color Feature Extraction . 4.4.1 Color Histogram. 4.4.1.1 Component Histogram. 4.4.1.2 Indexed Color Histogram. 4.4.1.3 Dominant Color Histogram. 4.4.2 Color Structure Descriptor. 4.4.3 Dominant
Color Descriptor. 4.4.4 Color Coherence Vector. 4.4.5 Color Correlogram. 4.4.6 Color Layout Descriptor. 59 59 60 60 64 66 71 73 74 74 75 77 77 78 79 79 80 82 84 85 87
Contents 5 6 XV 4.4.7 Scalable Color Descriptor. 4.4.8 Color Moments. 4.5 Image Enhancement. 4.5.1 Noise Removal. 4.5.2 Contrast Enhancement. 4.6 Summary. 4.7 Exercises. i. References. 87 88 90 90 90 96 97 99 Texture Feature Extraction. 5.1 Introduction. 5.2 Spatial Texture Feature Extraction Methods. 5.2.1 Tamura Textures. 5.2.2 Gray-Level Co-Occurrence Matrices. 5.2.3 Markov Random Field. 5.2.4 Fractal Dimension. 5.2.5 Discussions. 5.3 Spectral Texture Feature Extraction Methods. 5.3.1 DCT-Based Texture
Feature. 5.3.2 Texture Features Based on Gabor Filters. 5.3.2.1 Gabor Filters . 5.3.2.2 Gabor Spectrum. 5.3.2.3 Texture Representation. 5.3.2.4 Rotation Invariant Gabor Features. 5.3.3 Texture Features Based on WaveletTransform. 5.3.3.1 Selection and Application of Wavelets. 5.3.3.2 Contrast of DWT and Other Spectral Transforms. 5.3.3.3 Multiresolution Analysis. 5.3.4 Texture Features Based on CurveletTransform. 5.3.4.1 Curvelet Transform. 5.3.4.2 Discrete CurveletTransform. 5.3.4.3 Curvelet Spectra. 5.3.4.4 Curvelet Features. 5.3.5 Discussions. 5.4 Summary. 5.5 Exercises. References. 101 101 102 102 105 106 107 108 109 109 110 110 112 112 114 117 117 Shape
Representation. 6.1 Introduction. 6.2 Perceptual Shape Descriptors. 6.2.1 Circularity and Compactness. 6.2.2 Eccentricity and Elongation. 133 133 134 134 135 121 121 122 122 125 129 129 130 130 131 131
xvi Contents 6.2.3 Convexity and Solidarity. 6.2.4 Euler Number. 6.2.5 Bending Energy. 6.3 Contour-Based Shape Methods. 6.3.1 Shape Signatures. 6.3.1.1 Position Function. 6.3.1.2 Centroid Distance. 6.3.1.3 . Angular Functions. 6.3.1.4 Curvature Signature . 6.3.1.5 Area Function. 6.3.1.6 Discussions. 6.3.2 Shape Context. 6.3.3 Boundary Moments. 6.3.4 Stochastic Method. . . 6.3.5 Scale Space Method. 6.3.5.1 Scale Space. 6.3.5.2 Curvature Scale Space. 6.3.6 Fourier Descriptor. 6.3.7 Discussions. 6.3.8 Syntactic Analysis.
6.3.9 Polygon Decomposition. 6.3.9.1 Chain Code Representation. 6.3.9.2 Smooth Curve Decomposition. 6.3.9.3 Discussions. 6.4 Region-Based Shape Feature Extraction. 6.4.1 Geometric Moments. 6.4.2 Complex Moments. 6.4.3 Generic Fourier Descriptor. . 6.4.4 Shape Matrix. 6.4.5 Shape Profiles. 6.4.5.1 Shape Projections. 6.4.5.2 Radon Transform. 6.4.6 Discussions . 6.4.7 Convex Hull. 6.4.8 Medial Axis. 6.5 Summary. 6.6 Exercises. References. 136 137 138 139 139 139 140 141 142 145 146 146 148 148 149 149 149 152 152 153 154 156 156 157 157 158 159 162 165 166 166 167
170 171 172 173 174 175
xvii Contents Part Ш Image Classification and Annotation 7 Bayesian Classification. 7.1 Introduction. 7.2 Naïve Bayesian Image Classification. 7.2.1 NB Formulation. 7.2.2 NB with IndependentFeatures. 7.2.3 NB with Bag of Features. 7.3 Image Annotation with Word Co-occurrence. 7.4 Image Annotation with Joint Probability. 7.5 Cross-Media Relevance Model. 7.6 Image Annotation with Parametric Model. 7.7 Image Classification with Gaussian Process. 7.8 Summary. 7.9 Exercises. References. 183 183 185 185 188 188 188 191 193 194 196 198 199 200 8 Support Vector Machine. 8.1 Linear Classifier. 8.1.1 A Theoretical
Solution. 8.1.2 An Optimal Solution. 8.1.3 A Suboptimal Solution. 8.2 К Nearest Neighbor Classification. 8.3 Support Vector Machine. 8.3.1 The Perceptron. 8.3.2 SVM—The Primal Form. 8.3.2.1 The Margin Between Two Classes. 8.3.2.2 Margin Maximization. 8.3.2.3 The Primal Form of SVM. 8.3.3 The Dual Form of SVM. 8.3.3.1 The Dual-Form Perceptron. 8.3.4 Kernel-Based SVM . 8.3.4.1 The Dual-Form SVM Versus NN Classifier. 8.3.4.2 Kernel Definition. 8.3.4.3 Building New Kernels. 8.3.4.4 The Kernel Trick. 8.3.5 The Pyramid Match Kernel. 8.3.6 Discussions. 8.4 Fusion of SVMs. 8.4.1 Fusion of Binary SVMs
. 8.4.2 Multilevel Fusion of SVMs. 8.4.3 Fusion of SVMs with Different Features. 8.5 Summary. 201 201 202 203 204 205 206 207 208 208 210 211 211 213 213 213 215 217 218 220 223 223 223 224 224 226
xviii 9 Contents 8.6 Exercises. References. 226 228 Artificial Neural Network. 9.1 Introduction. 9.2 Artificial Neurons. 9.2.1 An AND Neuron. 9.2.2 An OR Neuron . 9.3 Perceptron . 9.4 Nonlinear Neural Network. 9.5 Activation and Inhibition. 9.5.1 Sigmoid Activation. 9.5.2 Shunting Inhibition. 9.6 The Backçropagation Neural Network . . . . . 9.6.1 The BP Network and ErrorFunction. 9.6.2 Layer К Weight Estimation and Updating. 9.6.3 Layer А-l Weight Estimation andUpdating. 9.6.4 The BP Algorithm . 9.7 Convolutional Neural Network. . 9.7.1 CNN
Architecture . 9.7.2 Input Layer. 9.7.3 Convolution Layer 1 (Cl). 9.7.3.1 2D Convolution. 9.7.3.2 Stride and Padding. 9.7.3.3 Bias . . 9.7.3.4 Volume Convolution in Layer Cl. 9.7.3.5Depth of the Feature Map Volume. 9.7.3.6 ReLU Activation. 9.7.3.7 Batch Normalization. 9.7.4 Pooling or Subsampling Layer 1 (SI). . 9.7.5 Convolution Layer 2 (C2). 9.7.6 Hyperparameters . 9.8 Implementation of CNN. 9.8.1 CNN Architecture. 9.8.2 Filters of the Convolution Layers. 9.8.3 Filters of the Fully Connected Layers. 9.8.4 Feature Maps of Convolution Layers. 9.8.5 Matlab Implementation. 9.9 Summary. 9.10
Exercises. References. 229 229 230 232 232 234 235 238 238 239 240 240 241 242 245 246 246 247 247 249 249 250 250 250 252 252 253 254 254 255 256 257 257 260 265 267 267 270
xix Contents 10 Image Annotation with Decision Tree. 10.1 Introduction . 10.2 ID3. 10.2.1 ID3 Splitting Criterion. 10.3 C4.5. 10.3.1 C4.5 Splitting Criterion. 10.4 CART. 10.4.1 Classification Tree Splitting Criterion. 10.4.2 Regression Tree Splitting Criterion. 10.4.3 Application of Regression Tree. 10.5 DT for Image Classification. 10.5.1 Feature Discretization. 10.5.2 Building the DT. 10.5.3 Image Classification and Annotation with DT. 10.6 Summary. 10.7 Exercises. References. Part IV 271 271 273 274 275 275 276 276 278 278 279 279 282 285 287 287 289 Image Retrieval and Presentation 11 Image
Indexing. 11.1 Numerical Indexing. . . 11.1.1 List Indexing. 11.1.2 Tree Indexing. 11.2 Inverted File Indexing. 11.2.1 Inverted File for TextualDocuments Indexing. 11.2.2 Inverted File for Image Indexing. 11.2.2.1 Determine the Area Weight aw. 11.2.2.2 Determine the Position Weight pw. 11.2.2.3 Determine theRelationship Weight rw. 11.2.2.4 Inverted Filefor Image Indexing. 11.3 Summary. 11.4 Exercises. References. . 293 293 293 294 295 295 297 297 298 298 299 300 300 301 12 Image Ranking. 12.1 Introduction . . . . 12.2 Similarity Measures. 12.2.1 Distance
Metric. 12.2.2 Minkowski-Form Distance. 12.2.3 Mass-Based Distance. . 12.2.4 Cosine Distance. 12.2.5 χ2 Statistic. 12.2.6 Histogram Intersection. 303 303 304 304 304 305 309 310 310
Contents XX 13 12.2.7 Quadratic Distance. 12.2.8 Mahalanobis Distance. 12.3 Performance Measures. 12.3.1 Recall and Precision Pair (RPP). 12.3.2 F-measure. 12.3.3 Percentage of Weighted Hits (PWH). 12.3.4 Percentage of Similarity Ranking (PSR). 12.3.5 Bullseye Accuracy. 12.4 Hypothesis Testing. 12.4.1 Introduction . . . 12.4.2 Fundamental Theorems of Statistics. 12.4.3 Properties of Normal Distribution. 12.4.4 HT on a Single Population. 12.4.5 Power of Test. 12.4.6 HT on Difference of Means. 12.4.7 Summary of HT. 12.4.8 Margin of Error. 12.5 Summary. 12.6 Exercises.
References. 311 312 313 314 316 317 318 319 319 319 320 321 323 326 327 329 329 331 331 333 Image Presentation. 13.1 Introduction. 13.2 Caption Browsing. 13.3 Category Browsing. 13.3.1 Category Browsing on the Web. 13.3.2 Hierarchical Category Browsing. 13.4 Content Browsing. 13.4.1 Content Browsing in 3D Space. 13.4.2 Content Browsing with Fish Eye View. 13.4.3 Force-Directed Content Browsing. 13.5 Query by Example. 13.6 Query by Keywords. 13.7 Summary. 13.8 Exercises. References. 335 335 335 336 337 339 339 341 341 344 344 347 350 351 352 Appendix: Deriving the Conditional Probability of a Gaussian Process. 353
Index. 359 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Zhang, Dengsheng |
| author_GND | (DE-588)1238716180 |
| author_facet | Zhang, Dengsheng |
| author_role | aut |
| author_sort | Zhang, Dengsheng |
| author_variant | d z dz |
| building | Verbundindex |
| bvnumber | BV047646014 |
| classification_rvk | ST 330 ST 530 |
| classification_tum | DAT 600f DAT 760f |
| ctrlnum | (OCoLC)1294760597 (DE-599)BVBBV047646014 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | Second edition |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01720nam a2200433 c 4500</leader><controlfield tag="001">BV047646014</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20230228 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">211217s2021 a||| b||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783030692506</subfield><subfield code="9">978-3-030-69250-6</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1294760597</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047646014</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-11</subfield><subfield code="a">DE-355</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 330</subfield><subfield code="0">(DE-625)143663:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 530</subfield><subfield code="0">(DE-625)143679:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 600f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 760f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Dengsheng</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1238716180</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fundamentals of image data mining</subfield><subfield code="b">analysis, features, classification and retrieval</subfield><subfield code="c">Dengsheng Zhang</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Second edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham, Switzerland</subfield><subfield code="b">Springer</subfield><subfield code="c">[2021]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xxxiii, 363 Seiten</subfield><subfield code="b">Illustrationen, Diagramme (teilweise farbig)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Texts in computer science</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Informatik</subfield><subfield code="0">(DE-588)4026894-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Multimedia data mining</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Informatik</subfield><subfield code="0">(DE-588)4026894-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-3-030-69251-3</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Regensburg - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033030147&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033030147</subfield></datafield></record></collection> |
| id | DE-604.BV047646014 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:48:47Z |
| indexdate | 2024-07-10T09:18:11Z |
| institution | BVB |
| isbn | 9783030692506 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033030147 |
| oclc_num | 1294760597 |
| open_access_boolean | |
| owner | DE-11 DE-355 DE-BY-UBR |
| owner_facet | DE-11 DE-355 DE-BY-UBR |
| physical | xxxiii, 363 Seiten Illustrationen, Diagramme (teilweise farbig) |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Springer |
| record_format | marc |
| series2 | Texts in computer science |
| spelling | Zhang, Dengsheng Verfasser (DE-588)1238716180 aut Fundamentals of image data mining analysis, features, classification and retrieval Dengsheng Zhang Second edition Cham, Switzerland Springer [2021] © 2021 xxxiii, 363 Seiten Illustrationen, Diagramme (teilweise farbig) txt rdacontent n rdamedia nc rdacarrier Texts in computer science Data Mining (DE-588)4428654-5 gnd rswk-swf Informatik (DE-588)4026894-9 gnd rswk-swf Multimedia data mining Data Mining (DE-588)4428654-5 s Informatik (DE-588)4026894-9 s DE-604 Erscheint auch als Online-Ausgabe 978-3-030-69251-3 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033030147&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zhang, Dengsheng Fundamentals of image data mining analysis, features, classification and retrieval Data Mining (DE-588)4428654-5 gnd Informatik (DE-588)4026894-9 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4026894-9 |
| title | Fundamentals of image data mining analysis, features, classification and retrieval |
| title_auth | Fundamentals of image data mining analysis, features, classification and retrieval |
| title_exact_search | Fundamentals of image data mining analysis, features, classification and retrieval |
| title_exact_search_txtP | Fundamentals of image data mining analysis, features, classification and retrieval |
| title_full | Fundamentals of image data mining analysis, features, classification and retrieval Dengsheng Zhang |
| title_fullStr | Fundamentals of image data mining analysis, features, classification and retrieval Dengsheng Zhang |
| title_full_unstemmed | Fundamentals of image data mining analysis, features, classification and retrieval Dengsheng Zhang |
| title_short | Fundamentals of image data mining |
| title_sort | fundamentals of image data mining analysis features classification and retrieval |
| title_sub | analysis, features, classification and retrieval |
| topic | Data Mining (DE-588)4428654-5 gnd Informatik (DE-588)4026894-9 gnd |
| topic_facet | Data Mining Informatik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033030147&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT zhangdengsheng fundamentalsofimagedatamininganalysisfeaturesclassificationandretrieval |