Machine learning for audio, image and video analysis: theory and applications
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
London ; Heidelberg [u.a.]
Springer
2015
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Advanced information and knowledge processing
|
| Schlagwörter: | |
| Links: | http://swbplus.bsz-bw.de/bsz444852654cov.htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028352929&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XVI, 561 S. Ill., graph. Darst. |
| ISBN: | 9781447167341 |
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| 245 | 1 | 0 | |a Machine learning for audio, image and video analysis |b theory and applications |c Francesco Camastra ; Alessandro Vinciarelli |
| 250 | |a 2. ed. | ||
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| 300 | |a XVI, 561 S. |b Ill., graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1819346027652579328 |
|---|---|
| adam_text | Contents
1 Introduction..................................................... 1
LJ Two Fundamental Questions.................................... 1
1.1.1 Why Should One Read the Book?.................... 1
1.1.2 What Is the Book About?............................. 2
1.2 The Structure of the Book.................................... 4
1.2.1 Part I: From Perception to Computation.............. 4
1.2.2 Part II: Machine Learning........................... 5
1.2.3 Part III: Applications.............................. 6
1.2.4 Appendices.......................................... 7
1.3 How to Read This Book........................................ 8
L3.1 Background and Learning Objectives.................. 8
1.3.2 Difficulty Level.................................... 8
1.3.3 Problems............................................ 9
1.3.4 Software............................................ 9
1.4 Reading Tracks............................................... 9
Part I From Perception to Computation
2 Audio Acquisition, Representation and Storage..................... 13
2.1 Introduction.................................................. 13
2.2 Sound Physics, Production and Perception..................... 15
2.2.1 Acoustic Waves Physics.............................. 15
2.2.2 Speech Production................................... 18
2.2.3 Sound Perception.................................... 20
2.3 Audio Acquisition............................................. 22
2.3.1 Sampling and Aliasing............................... 23
2.3.2 The Sampling Theorem**.............................. 25
2.3.3 Linear Quantization................................. 28
2.3.4 Nonuniform Scalar Quantization...................... 30
vii
Contents
viii
2.4 Audio Encoding and Storage Formats............................ 32
2.4.1 Linear PCM and Compact Discs........................ 33
2.4.2 MPEG Digital Audio Coding........................... 34
2.4.3 AAC Digital Audio Coding............................ 35
2.4.4 Perceptual Coding................................... 36
2.5 Time-Domain Audio Processing................................ 38
2.5.1 Linear and Time-Invariant Systems................... 39
2.5.2 Short-Term Analysis................................. 40
2.5.3 Time-Domain Measures................................ 43
2.6 Linear Predictive Coding...................................... 47
2.6.1 Parameter Estimation................................ 50
2.7 Conclusions................................................... 52
Problems............................................................. 52
References........................................................... 53
3 Image and Video Acquisition, Representation and Storage.............. 57
3.1 Introduction.................................................. 57
3.2 Human Eye Physiology.......................................... 58
3.2.1 Structure of the Human Eye........................ 58
3.3 Image Acquisition Devices..................................... 60
3.3.1 Digital Camera...................................... 60
3.4 Color Representation.......................................... 63
3.4.1 Human Color Perception.............................. 63
3.4.2 Color Models....................................... 64
3.5 Image Formats................................................. 76
3.5.1 Image File Format Standards......................... 76
3.5.2 JPEG Standard....................................... 77
3.6 Image Descriptors............................................. 81
3.6.1 Global Image Descriptors............................ 81
3.6.2 SIFT Descriptors.................................... 85
3.7 Video Principles.............................................. 88
3.8 MPEG Standard................................................. 89
3.8.1 Further MPEG Standards.............................. 90
3.9 Conclusions................................................... 93
Problems............................................................. 93
References........................................................... 95
Part II Machine Learning
4 Machine Learning............................................. 99
4.1 Introduction.......................................... 99
4.2 Taxonomy of Machine Learning.......................... 100
Contents ix
4.2.1 Rote Learning....................................... 100
4.2.2 Learning from Instruction........................... 101
4.2.3 Learning by Analogy................................. 101
4.3 Learning from Examples....................................... 101
4.3.1 Supervised Learning................................. 102
4.3.2 Reinforcement Learning.............................. 103
4.3.3 Unsupervised Learning............................... 103
4.3.4 Semi-supervised Learning............................ 104
4.4 Conclusions.................................................. 105
References........................................................... 105
5 Bayesian Theory of Decision.......................................... 107
5.1 Introduction................................................. 107
5.2 Bayes Decision Rule.......................................... 108
5.3 Bayes Classifier*............................................ 110
5.4 Loss Function................................................ 112
5.4.1 Binary Classification............................... 114
5.5 Zero-One Loss Function....................................... 115
5.6 Discriminant Functions....................................... 116
5.6.1 Binary Classification Case.......................... 117
5.7 Gaussian Density............................................. 118
5.7.1 Univariate Gaussian Density......................... 118
5.7.2 Multivariate Gaussian Density....................... 119
5.7.3 Whitening Transformation............................ 120
5.8 Discriminant Functions for Gaussian Likelihood............... 122
5.8.1 Features Are Statistically Independent.............. 122
5.8.2 Covariance Matrix Is the Same for All Classes .... 123
5.8.3 Covariance Matrix Is Not the Same
for All Classes..................................... 125
5.9 Receiver Operating Curves.................................... 125
5.10 Conclusions.................................................. 127
Problems. . ......................................................... 128
References........................................................... 129
6 Clustering Methods................................................... 131
6.1 Introduction................................................. 131
6.2 Expectation and Maximization Algorithm*...................... 133
6.2.1 Basic EM*........................................... 134
6.3 Basic Notions and Terminology................................ 136
6.3.1 Codebooks and Codevectors........................... 136
6.3.2 Quantization Error Minimization..................... 137
6.3.3 Entropy Maximization................................ 138
6.3.4 Vector Quantization................................. 139
Contents
6.4 K-Means..................................................... 141
6.4.1 Batch K-Means.................................... 142
6.4.2 Online K-Means..................................... 143
6.4.3 K-Means Software Packages........................ 146
6.5 Self-Organizing Maps........................................ 146
6.5.1 SOM Software Packages............................ 148
6.5.2 SOM Drawbacks.................................... 148
6.6 Neural Gas and Topology Representing Network................ 149
6.6.1 Neural Gas......................................... 149
6.6.2 Topology Representing Network...................... 150
6.6.3 Neural Gas and TRN Software Package................ 151
6.6.4 Neural Gas and TRN Drawbacks....................... 151
6.7 General Topographic Mapping*................................ 151
6.7.1 Latent Variables*.................................. 152
6.7.2 Optimization by EM Algorithm*...................... 153
6.7.3 GTM Versus SOM*................................. 154
6.7.4 GTM Software Package............................ 155
6.8 Fuzzy Clustering Algorithms................................. 155
6.8.1 FCM................................................ 156
6.9 Hierarchical Clustering..................................... 157
6.10 Mixtures of Gaussians....................................... 159
6.10.1 The E-Step......................................... 160
6.10.2 The M-Step......................................... 161
6.11 Conclusion.................................................. 163
Problems............................................................ 164
References.......................................................... 165
Foundations of Statistical Learning and Model Selection............. 169
7.1 Introduction................................................ 169
7.2 Bias-Variance Dilemma....................................... 170
7.2.1 Bias-Variance Dilemma for Regression............... 170
7.2.2 Bias-Variance Decomposition
for Classification*................................ 171
7.3 Model Complexity............................................ 173
7.4 VC Dimension and Structural Risk Minimization............... 176
7.5 Statistical Learning Theory*................................ 179
7.5.1 Vapnik-Chervonenkis Theory......................... 180
7.6 AIC and BIC Criteria........................................ 182
7.6.1 Akaike Information Criterion....................... 182
7.6.2 Bayesian Information Criterion..................... 183
7.7 Minimum Description Length Approach.................... 184
Contents xi
7.8 Crossvalidation............................................... 186
7.8.1 Generalized Crossvalidation......................... 186
7.9 Conclusion.................................................... 188
Problems............................................................. 188
References........................................................... 189
8 Supervised Neural Networks and Ensemble Methods...................... 191
8.1 Introduction.................................................. 191
8.2 Artificial Neural Networks and Neural Computation............. 192
8.3 Artificial Neurons............................................ 193
8.4 Connections and Network Architectures......................... 196
8.5 Single-Layer Networks......................................... 198
8.5.1 Linear Discriminant Functions and Single-Layer
Networks............................................ 199
8.5.2 Linear Discriminants and the Logistic Sigmoid .... 200
8.5.3 Generalized Linear Discriminants
and the Perceptron.................................. 201
8.6 Multilayer Networks........................................... 203
8.6.1 The Multilayer Perceptron........................... 204
8.7 Multilayer Networks Training.................................. 205
8.7.1 Error Back-Propagation for Feed-Forwards
Networks*.......................................... 206
8.7.2 Parameter Update: The Error Surface................. 208
8.7.3 Parameters Update: The Gradient Descent*............ 210
8.7.4 The Torch Package................................... 212
8.8 Learning Vector Quantization.................................. 212
8.8.1 The LVQ_PAK Software Package........................ 214
8.9 Nearest Neighbour Classification.............................. 215
8.9.1 Probabilistic Interpretation........................ 217
8.10 Ensemble Methods.............................................. 217
8.10.1 Classifier Diversity and Ensemble Performance* ... 218
8.10.2 Creating Ensemble of Diverse Classifiers............ 220
8.11 Conclusions................................................... 224
Problems............................................................. 224
References......................................................... 225 9
9 Kernel Methods....................................................... 229
9.1 Introduction................................................. 229
9.2 Lagrange Method and Kuhn Tucker Theorem...................... 231
9.2.1 Lagrange Multipliers Method......................... 231
9.2.2 Kuhn Tucker Theorem................................. 233
9.3 Support Vector Machines for Classification................... 235
9.3.1 Optimal Hyperplane Algorithm........................ 236
9.3.2 Support Vector Machine Construction................. 238
Xll
Contents
9.3.3 Algorithmic Approaches to Solve Quadratic
Programming...................................... 241
9.3.4 Sequential Minimal Optimization.................... 242
9.3.5 Other Optimization Algorithms...................... 244
9.3.6 SVM and Regularization Methods*.................... 244
9.4 Multiclass Support Vector Machines.......................... 247
9.4.1 One-Versus-Rest Method............................. 247
9.4.2 One-Versus-One Method.............................. 247
9.4.3 Other Methods...................................... 248
9.5 Support Vector Machines for Regression...................... 248
9.5.1 Regression with Quadratic 6-Insensitive Loss..... 249
9.5.2 Kernel Ridge Regression............................ 252
9.5.3 Regression with Linear 6-Insensitive Loss.......... 254
9.5.4 Other Approaches to Support Vector Regression . . . 256
9.6 Gaussian Processes.......................................... 256
9.6.1 Regression with Gaussian Processes................. 257
9.7 Kernel Fisher Discriminant.................................. 258
9.7.1 Fisher’s Linear Discriminant....................... 258
9.7.2 Fisher Discriminant in Feature Space............... 260
9.8 Kernel PC A................................................. 262
9.8.1 Centering in Feature Space......................... 262
9.9 One-Class SVM............................................... 264
9.9.1 One-Class SVM Optimization......................... 267
9.10 Kernel Clustering Methods................................... 269
9.10.1 Kernel K-Means..................................... 270
9.10.2 Kernel SOM......................................... 272
9.10.3 Kernel Neural Gas.................................. 272
9.10.4 One-Class SVM Extensions........................... 273
9.10.5 Kernel Fuzzy Clustering Methods.................... 274
9.11 Spectral Clustering......................................... 278
9.11.1 Shi and Malik Algorithm............................ 280
9.11.2 Ng-Jordan-Weiss’Algorithm.......................... 281
9.11.3 Other Methods...................................... 282
9.11.4 Connection Between Spectral and Kernel
Clustering Methods................................. 283
9.12 Software Packages........................................... 287
9.13 Conclusion.................................................. 287
Problems............................................................ 288
References....................................................... 289 10
10 Markovian Models for Sequential Data............................. 295
10.1 Introduction................................................ 295
10.2 Hidden Markov Models........................................ 296
10.2.1 Emission Probability Functions..................... 300
Contents xiii
10.3 The Three Problems............................................ 300
10.4 The Likelihood Problem and the Trellis**...................... 301
10.5 The Decoding Problem**........................................ 304
10.6 The Learning Problem**........................................ 308
10.6.1 Parameter Initialization............................ 309
10.6.2 Estimation of the Initial State Probabilities....... 310
10.6.3 Estimation of the Transition Probabilities.......... 311
10.6.4 Emission Probability Function Parameters
Estimation.......................................... 312
10.7 HMM Variants.................................................. 315
10.8 Linear-Chain Conditional Random Fields........................ 317
10.8.1 From HMMs to Linear-Chain CRFs...................... 319
10.8.2 General CRFs........................................ 321
10.8.3 The Three Problems.................................. 322
10.9 The Inference Problem for Linear Chain CRFs................... 323
10.10 The Training Problem for Linear Chain CRFs.................... 323
10.11 V-gram Models and Statistical Language Modeling............... 325
10.11.1 TV-gram Models...................................... 325
10.11.2 The Perplexity...................................... 326
10.11.3 TV-grams Parameter Estimation....................... 327
10.11.4 The Sparseness Problem
and the Language Case............................... 328
10.12 Discounting and Smoothing Methods
for V-gram Models**.......................................... 330
10.12.1 The Leaving-One-Out Method.......................... 331
10.12.2 The Turing Good Estimates........................... 333
10.12.3 Katz’s Discounting Model............................ 334
10.13 Building a Language Model with yV-grams...................... 336
Problems............................................................. 337
References...................................................... 338 11 *
11 Feature Extraction Methods and Manifold Learning Methods ... 341
11.1 Introduction.................................................. 341
11.2 *The Curse of Dimensionality.................................. 343
11.3 Data Dimensionality........................................... 344
11.3.1 Local Methods....................................... 345
11.3.2 Global Methods...................................... 347
11.3.3 Mixed Methods....................................... 355
11.4 Principal Component Analysis.................................. 357
11.4.1 PCA as ID Estimator.................................. 359
11.4.2 Nonlinear Principal Component Analysis.............. 361
11.5 Independent Component Analysis................................ 362
11.5.1 Statistical Independence............................ 363
11.5.2 ICA Estimation...................................... 364
XIV
Contents
11.5.3 ICA by Mutual Information Minimization.......... 367
11.5.4 FastICA Algorithm............................... 369
11.6 Multidimensional Scaling Methods......................... 370
11.6.1 Sammon’s Mapping................................ 371
11.7 Manifold Learning........................................ 372
11.7.1 The Manifold Learning Problem................... 372
11.7.2 Isomap.......................................... 374
11.7.3 Locally Linear Embedding........................ 375
11.7.4 Laplacian Eigenmaps............................. 378
11.8 Conclusion............................................... 379
Problems........................................................ 379
References...................................................... 381
Part III Applications
12 Speech and Handwriting Recognition................................ 389
12.1 Introduction................................................ 389
12.2 The General Approach........................................ 390
12.3 The Front End............................................... 392
12.3.1 The Handwriting Front End......................... 393
12.3.2 The Speech Front End.............................. 394
12.4 HMM Training................................................ 397
12.4.1 Lexicon and Training Set.......................... 397
12.4.2 Hidden Markov Models Training..................... 398
12.5 Recognition and Performance Measures........................ 400
12.5.1 Recognition....................................... 400
12.5.2 Performance Measurement........................... 401
12.6 Recognition Experiments..................................... 403
12.6.1 Lexicon Selection................................. 404
12.6.2 Лґ-gram Model Performance......................... 405
12.6.3 Cambridge Database Results........................ 407
12.6.4 IAM Database Results.............................. 408
12.7 Speech Recognition Results.................................. 409
12.8 Applications................................................ 411
12.8.1 Applications of Handwriting Recognition........... 411
12.8.2 Applications of Speech Recognition................ 413
References........................................................ 415 13 *
13 Automatic Face Recognition......................................... 421
13.1 Introduction................................................ 421
13.2 Face Recognition: General Approach........................... 423
13.3 Face Detection and Localization.............................. 424
Contents
XV
13.3.1 Face Segmentation and Normalization
with TorchVision.................................... 426
13.4 Lighting Normalization....................................... 428
13.4.1 Center/Surround Retinex............................. 428
13.4.2 Gross and Brajovic’s Algorithm...................... 429
13.4.3 Normalization with TorchVision...................... 429
13.5 Feature Extraction........................................... 430
13.5.1 Holistic Approaches................................. 430
13.5.2 Local Approaches.................................... 434
13.5.3 Feature Extraction with TorchVision................. 434
13.6 Classification............................................... 437
13.7 Performance Assessment....................................... 439
13.7.1 The FERET Database.................................. 440
13.7.2 The FRVT Database................................... 441
13.8 Experiments.................................................. 442
13.8.1 Data and Experimental Protocol...................... 443
13.8.2 Euclidean Distance-Based Classifier................. 443
13.8.3 SVM-Based Classification............................ 445
References.......................................................... 445
14 Video Segmentation and Keyframe Extraction........................... 449
14.1 Introduction................................................. 449
14.2 Applications of Video Segmentation........................... 451
14.3 Shot Boundary Detection...................................... 452
14.3.1 Pixel-Based Approaches.............................. 453
14.3.2 Block-Based Approaches.............................. 455
14.3.3 Histogram-Based Approaches.......................... 455
14.3.4 Clustering-Based Approaches......................... 456
14.3.5 Performance Measures................................ 457
14.4 Shot Boundary Detection with Torchvision..................... 458
14.5 Keyframe Extraction.......................................... 460
14.6 Keyframe Extraction with Torchvision and Torch............... 462
References....................................................... 463 15
15 Real-Time Hand Pose Recognition.................................... 467
15.1 Introduction................................................. 467
15.2 Hand Pose Recognition Methods................................ 468
15.3 Hand Pose Recognition by a Data Glove........................ 471
15.4 Hand Pose Color-Based Recognition............................ 475
15.4.1 Segmentation Module................................ 476
15.4.2 Feature Extraction.................................. 478
15.4.3 The Classifier...................................... 479
15.4.4 Experimental Results............................... 480
References.......................................................... 483
xvi Contents
16 Automatic Personality Perception.................................... 485
16.1 Introduction................................................. 485
16.2 Previous Work................................................ 486
16.2.1 Nonverbal Behaviour................................ 487
16.2.2 Social Media....................................... 488
16.3 Personality and Its Measurement.............................. 488
16.4 Speech-Based Automatic Personality Perception................ 490
16.4.1 The SSPNet Speaker Personality Corpus.............. 491
16.4.2 The Approach....................................... 492
16.4.3 Extraction of Short-Term Features.................. 492
16.4.4 Extraction of Statisticals......................... 493
16.4.5 Prediction......................................... 493
16.5 Experiments and Results...................................... 494
16.6 Conclusions.................................................. 496
References.......................................................... 497
Part IV Appendices
Appendix A: Statistics................................................... 501
Appendix B: Signal Processing............................................ 513
Appendix C: Matrix Algebra............................................... 525
Appendix D: Mathematical Foundations of Kernel Methods................... 531
Index
551
|
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| author | Camastra, Francesco Vinciarelli, Alessandro |
| author_facet | Camastra, Francesco Vinciarelli, Alessandro |
| author_role | aut aut |
| author_sort | Camastra, Francesco |
| author_variant | f c fc a v av |
| building | Verbundindex |
| bvnumber | BV042925725 |
| classification_rvk | ST 304 |
| ctrlnum | (OCoLC)923377343 (DE-599)BSZ444852654 |
| discipline | Informatik |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV042925725 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T17:23:26Z |
| institution | BVB |
| isbn | 9781447167341 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028352929 |
| oclc_num | 923377343 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-29T DE-11 |
| owner_facet | DE-355 DE-BY-UBR DE-29T DE-11 |
| physical | XVI, 561 S. Ill., graph. Darst. |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Springer |
| record_format | marc |
| series2 | Advanced information and knowledge processing |
| spellingShingle | Camastra, Francesco Vinciarelli, Alessandro Machine learning for audio, image and video analysis theory and applications Maschinelles Sehen (DE-588)4129594-8 gnd Digitale Signalverarbeitung (DE-588)4113314-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Informatik (DE-588)4026894-9 gnd Bildanalyse (DE-588)4145391-8 gnd Videobearbeitung (DE-588)4536854-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Digitales Tonsignal (DE-588)4229078-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| subject_GND | (DE-588)4129594-8 (DE-588)4113314-6 (DE-588)4193754-5 (DE-588)4026894-9 (DE-588)4145391-8 (DE-588)4536854-5 (DE-588)4033447-8 (DE-588)4229078-8 (DE-588)4006684-8 |
| title | Machine learning for audio, image and video analysis theory and applications |
| title_auth | Machine learning for audio, image and video analysis theory and applications |
| title_exact_search | Machine learning for audio, image and video analysis theory and applications |
| title_full | Machine learning for audio, image and video analysis theory and applications Francesco Camastra ; Alessandro Vinciarelli |
| title_fullStr | Machine learning for audio, image and video analysis theory and applications Francesco Camastra ; Alessandro Vinciarelli |
| title_full_unstemmed | Machine learning for audio, image and video analysis theory and applications Francesco Camastra ; Alessandro Vinciarelli |
| title_short | Machine learning for audio, image and video analysis |
| title_sort | machine learning for audio image and video analysis theory and applications |
| title_sub | theory and applications |
| topic | Maschinelles Sehen (DE-588)4129594-8 gnd Digitale Signalverarbeitung (DE-588)4113314-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Informatik (DE-588)4026894-9 gnd Bildanalyse (DE-588)4145391-8 gnd Videobearbeitung (DE-588)4536854-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Digitales Tonsignal (DE-588)4229078-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| topic_facet | Maschinelles Sehen Digitale Signalverarbeitung Maschinelles Lernen Informatik Bildanalyse Videobearbeitung Künstliche Intelligenz Digitales Tonsignal Bildverarbeitung |
| url | http://swbplus.bsz-bw.de/bsz444852654cov.htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028352929&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT camastrafrancesco machinelearningforaudioimageandvideoanalysistheoryandapplications AT vinciarellialessandro machinelearningforaudioimageandvideoanalysistheoryandapplications |