Verfügbarkeit: Machine Learning for audio, image and video analysis

Machine Learning for audio, image and video analysis: theory and applications

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Bibliographische Detailangaben
Beteiligte Personen:	Camastra, Francesco (VerfasserIn), Vinciarelli, Alessandro (VerfasserIn)
Format:	Medienkombination Buch
Sprache:	Englisch
Veröffentlicht:	London Springer 2008
Schriftenreihe:	Advanced Information and Knowledge Processing
Schlagwörter:	Informatik Videobearbeitung Bildanalyse Digitale Signalverarbeitung Digitales Tonsignal Künstliche Intelligenz Maschinelles Lernen Maschinelles Sehen Bildverarbeitung
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016406357&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	XVI, 494 S. Ill., graph. Darst.
ISBN:	9781848000063 9781848000070

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Datensatz im Suchindex

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adam_text	Contents 1 Introduction ............................................... 1 1.1 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 ................................ 7 1.3.1 Background and Learning Objectives .............. 8 1.3.2 Difficulty Level ................................. 8 1.3.3 Problems ...................................... 8 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 ............................. 27 2.3.4 Nonuniform Scalar Quantization .................. 30 2.4 Audio Encoding and Storage Formats .................... 32 VIII Contents 2.4.1 Linear PCM and Compact Discs .................. 32 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 ............... 38 2.5.2 Short-Term Analysis ............................ 40 2.5.3 Time-Domain Measures ......................... 41 Problems .................................................... 46 References ..................................................... 49 3 Image and Video Acquisition, Representation and Storage . 51 3.1 Introduction .......................................... 51 3.2 Human Eye Physiology ................................. 52 3.2.1 Structure of the Human Eye ..................... 52 3.3 Image Acquisition Devices .............................. 54 3.3.1 Digital Camera ................................. 54 3.4 Color Representation ................................... 57 3.4.1 Human Color Perception ........................ 57 3.4.2 Color Models ................................... 59 3.5 Image Formats ........................................ 66 3.5.1 Image File Format Standards ..................... 66 3.5.2 JPEG Standard ................................ 68 3.6 Video Principles ....................................... 72 3.7 MPEG Standard ....................................... 73 3.7.1 Further MPEG Standards ....................... 75 3.8 Conclusions ........................................... 77 Problems .................................................... 78 References ..................................................... 79 Part II Machine Learning 4 Machine Learning .......................................... 83 4.1 Introduction .......................................... 83 4.2 Taxonomy of Machine Learning .......................... 84 4.2.1 Rote Learning .................................. 84 4.2.2 Learning from Instruction ........................ 85 4.2.3 Learning by Analogy ............................ 85 4.3 Learning from Examples ................................ 85 4.3.1 Supervised Learning ............................ 86 4.3.2 Reinforcement Learning ......................... 86 4.3.3 Unsupervised Learning .......................... 87 Contents IX 4.4 Conclusions ........................................... 88 References ..................................................... 89 5 Bayesian Theory of Decision ............................... 91 5.1 Introduction .......................................... 91 5.2 Bayes Decision Rule .................................... 92 5.3 Bayes Classifier* ....................................... 95 5.4 Loss Function ......................................... 96 5.4.1 Binary Classification ............................ 98 5.5 Zero-One Loss Function ................................ 99 5.6 Discriminant Functions .................................100 5.6.1 Binary Classification Case .......................101 5.7 Gaussian Density ......................................101 5.7.1 Univariate Gaussian Density .....................102 5.7.2 Multivariate Gaussian Density ....................102 5.7.3 Whitening Transformation .......................104 5.8 Discriminant Functions for Gaussian Likelihood ............106 5.8.1 Features Are Statistically Independent ............106 5.8.2 Covariance Matrix Is The Same for All Classes .....107 5.8.3 Covariance Matrix Is Not the Same for All Classes .. 109 5.9 Receiver Operating Curves ..............................109 5.10 Conclusions ........................................... Ill Problems ....................................................112 References .....................................................115 6 Clustering Methods ........................................117 6.1 Introduction ..........................................117 6.2 Expectation and Maximization Algorithm* ................119 6.2.1 Basic EM* .....................................120 6.3 Basic Notions and Terminology ..........................122 6.3.1 Codebooks and Codevectors ......................122 6.3.2 Quantization Error Minimization .................124 6.3.3 Entropy Maximization ...........................124 6.3.4 Vector Quantization .............................125 6.4 K-Means ..............................................127 6.4.1 Batch K-Means .................................128 6.4.2 Online K-Means ................................129 6.4.3 K-Means Software Packages ......................132 6.5 Self-Organizing Maps ...................................132 6.5.1 SOM Software Packages .........................134 6.5.2 SOM Drawbacks ................................134 6.6 Neural Gas and Topology Representing Network ...........134 6.6.1 Neural Gas ....................................135 X Contents 6.6.2 Topology Representing Network ..................135 6.6.3 Neural Gas and TRN Software Package ............137 6.6.4 Neural Gas and TRN Drawbacks .................137 6.7 General Topographic Mapping* ..........................137 6.7.1 Latent Variables* ...............................137 6.7.2 Optimization by EM Algorithm* ..................139 6.7.3 GTM versus SOM* .............................140 6.7.4 GTM Software Package ..........................141 6.8 Fuzzy Clustering Algorithms ............................141 6.8.1 FCM ..........................................142 6.9 Hierarchical Clustering .................................142 6.10 Conclusion ............................................144 Problems ....................................................145 References .....................................................147 7 Foundations of Statistical Learning and Model Selection .......................................149 7.1 Introduction ..........................................149 7.2 Bias-Variance Dilemma .................................150 7.2.1 Bias-Variance Dilemma for Regression .............150 7.2.2 Bias-Variance Decomposition for Classification* .... 151 7.3 Model Complexity .....................................153 7.4 VC Dimension and Structural Risk Minimization ..........156 7.5 Statistical Learning Theory* ............................159 7.5.1 Vapnik-Chervonenkis Theory .....................161 7.6 AIC and BIC Criteria ..................................163 7.6.1 Akaiké Information Criterion .....................163 7.6.2 Bayesian Information Criterion ...................164 7.7 Minimum Description Length Approach ..................165 7.8 Crossvalidation ........................................166 7.8.1 Generalized Crossvalidation ......................166 7.9 Conclusion ............................................168 Problems ....................................................168 References .....................................................171 8 Supervised Neural Networks and Ensemble Methods ....................................173 8.1 Introduction ..........................................173 8.2 Artificial Neural Networks and Neural Computation ........174 8.3 Artificial Neurons ......................................175 8.4 Connections and Network Architectures ..................179 8.5 Single-Layer Networks ..................................180 Contents XI 8.5.1 Linear Discriminant Functions and Single-Layer Networks ......................................181 8.5.2 Linear Discriminants and the Logistic Sigmoid .....182 8.5.3 Generalized Linear Discriminants and the PerceptronlSS 8.Q Multilayer Networks ....................................186 8.6.1 The Multilayer Perceptron .......................186 8.7 Multilayer Networks Training ............................188 8.7.1 Error Back-Propagation for Feed-Forwards Networks188 8.7.2 Parameter Update: The Error Surface .............190 8.7.3 Parameters Update: The Gradient Descent ........192 8.7.4 The Torch Package ..............................194 8.8 Learning Vector Quantization ...........................194 8.8.1 The LVQ-PAK Software Package .................196 8.9 Ensemble Methods .....................................197 8.9.1 Classifier Diversity and Ensemble Performance* __. 198 8.9.2 Creating Ensemble of Diverse Classifiers ...........200 8.10 Conclusions ...........................................204 Problems ....................................................204 References .....................................................207 9 Kernel Methods ............................................211 9.1 Introduction ..........................................211 9.2 Lagrange Method and Kuhn Tucker Theorem .............213 9.2.1 Lagrange Multipliers Method .....................213 9.2.2 Kuhn Tucker Theorem ..........................215 9.3 Support Vector Machines for Classification ................216 9.3.1 Optimal Hyperplane Algorithm ...................217 9.3.2 Support Vector Machine Construction .............220 9.3.3 Algorithmic Approaches to Solve Quadratic Programming ..................................222 9.3.4 Sequential Minimal Optimization .................223 9.3.5 Other Optimization Algorithms ...................225 9.3.6 SVM and Regularization Methods* ...............226 9.4 Multiclass Support Vector Machines ......................228 9.4.1 One-versus-Rest Method .........................228 9.4.2 One-versus-One Method .........................229 9.4.3 Other Methods .................................229 9.5 Support Vector Machines for Regression ..................229 9.5.1 Regression with Quadratic е -Insensitive Loss .......230 9.5.2 Kernel Ridge Regression .........................233 9.5.3 Regression with Linear є -Insensitive Loss ..........235 9.5.4 Other Approaches to Support Vector Regression ___236 9.6 Gaussian Processes .....................................237 9.6.1 Regression with Gaussian Processes ...............238 XII Contents 9.7 Kernel Fisher Discriminant ..............................239 9.7.1 Fisher s Linear Discriminant .....................239 9.7.2 Fisher Discriminant in Feature Space ..............240 9.8 Kernel PCA ...........................................242 9.8.1 Centering in Feature Space .......................243 9.9 One-Class SVM .......................................245 9.9.1 One-Class SVM Optimization ....................247 9.10 Kernel Clustering Methods ..............................249 9.10.1 Kernel K-Means ................................249 9.10.2 One-Class SVM Extensions ......................251 9.10.3 Spectral Clustering .............................252 9.11 Software Packages .....................................254 9.12 Conclusion ............................................255 Problems ....................................................256 References .....................................................259 10 Markovian Models for Sequential Data .....................265 10.1 Introduction ..........................................265 10.2 Hidden Markov Models .................................266 10.2.1 Emission Probability Functions ...................269 10.3 The Three Problems ...................................270 10.4 The Likelihood Problem and the Trellis .................271 10.5 The Decoding Problem ...............................274 10.6 The Learning Problem ................................278 10.6.1 Parameter Initialization .........................279 10.6.2 Estimation of the Initial State Probabilities ........280 10.6.3 Estimation of the Transition Probabilities ..........280 10.6.4 Emission Probability Function Parameters Estimation .....................................281 10.7 HMM Variants ........................................284 10.8 iV-gram Models and Statistical Language Modeling ........286 10.8.1 JV-gram Models ................................287 10.8.2 The Perplexity .................................287 10.8.3 iV-grams Parameter Estimation ...................288 10.8.4 The Sparseness Problem and the Language Case ___289 10.9 Discounting and Smoothing Methods for iV-gram Models ..................................292 10.9.1 The Leaving-One-Out Method ....................292 10.9.2 The Turing Good Estimates ......................294 10.9.3 Katz s Disconting Model .........................295 10.10 Building a Language Model with iV-grams ................296 Problems ....................................................297 References .....................................................301 Contents XIII 11 Feature Extraction Methods and Manifold Learning Methods ...................................................305 11.1 Introduction ..........................................305 11.2 The Curse of Dimensionality* ...........................307 11.3 Data Dimensionality ...................................308 11.3.1 Local Methods .................................308 11.3.2 Global Methods ................................309 11.4 Principal Component Analysis ...........................313 11.4.1 Nonlinear Principal Component Analysis ..........315 11.5 Independent Component Analysis ........................316 11.5.1 Statistical Independence .........................318 11.5.2 ICA Estimation ................................318 11.5.3 ICA by Mutual Information Minimization .........322 11.5.4 FastICA Algorithm .............................323 11.6 Multidimensional Scaling Methods .......................325 11.6.1 Sammon s Mapping .............................325 11.7 Manifold Learning .....................................326 11.7.1 The Manifold Learning Problem ..................327 11.7.2 Isomap........................................328 11.7.3 Locally Linear Embedding .......................329 11.7.4 Laplacian Eigenmaps ............................331 11.8 Conclusion ............................................333 Problems ....................................................333 References .....................................................337 Part III Applications 12 Speech and Handwriting Recognition ......................345 12.1 Introduction ..........................................345 12.2 The General Approach .................................346 12.3 The Front End ........................................349 12.3.1 The Handwriting Front End ......................349 12.3.2 The Speech Front End ..........................351 12.4 HMM Training ........................................353 12.4.1 Lexicon and Training Set ........................353 12.4.2 Hidden Markov Models Training ..................355 12.5 Recognition and Performance Measures ...................356 12.5.1 Recognition ....................................356 12.5.2 Performance Measurement .......................357 12.6 Recognition Experiments ...............................359 12.6.1 Lexicon Selection ...............................360 12.6.2 JV-gram Model Performance ......................361 12.6.3 Cambridge Database Results .....................363 XIV Contents 12.6.4 IAM Database Results ..........................366 12.7 Speech Recognition Results .............................367 12.8 Applications ..........................................368 12.8.1 Applications of Handwriting Recognition ..........369 12.8.2 Applications of Speech Recognition ...............371 References .....................................................373 13 Automatic Face Recognition ...............................381 13.1 Introduction ..........................................381 13.2 Face Recognition: General Approach .....................383 13.3 Face Detection and Localization .........................385 13.3.1 Face Segmentation and Normalization with TorchVision ...............................387 13.4 Lighting Normalization .................................387 13.4.1 Center/Surround Retinex ........................388 13.4.2 Gross and Brajovic s Algorithm ..................389 13.4.3 Normalization with TorchVision ..................389 13.5 Feature Extraction .....................................390 13.5.1 Holistic Approaches .............................390 13.5.2 Local Approaches ...............................392 13.5.3 Feature Extraction with TorchVision ..............393 13.6 Classification ..........................................397 13.7 Performance Assessment ................................399 13.7.1 The FERET Database ..........................400 13.7.2 The FRVT database ............................401 13.8 Experiments ..........................................402 13.8.1 Data and Experimental Protocol ..................402 13.8.2 Euclidean Distance-Based Classifier ...............403 13.8.3 SVM-Based Classification ........................405 References .....................................................407 14 Video Segmentation and Keyframe Extraction .............413 14.1 Introduction ..........................................413 14.2 Applications of Video Segmentation ......................414 14.3 Shot Boundary Detection ...............................416 14.3.1 Pixel-Based Approaches .........................417 14.3.2 Block-Based Approaches .........................418 14.3.3 Histogram-Based Approaches .....................420 14.3.4 Clustering-Based Approaches .....................420 14.3.5 Performance Measures ...........................422 14.4 Shot Boundary Detection with Torchvision ................423 14.5 Keyframe Extraction ...................................424 14.6 Keyframe Extraction with Torchvision and Torch .......... 426 Contents XV References .....................................................427 Part IV Appendices A Statistics ...................................................433 A.I Fundamentals .........................................433 A. 1.1 Probability and Relative Frequency ...............433 A.1.2 The Sample Space ..............................434 A.1.3 The Addition Law ..............................434 A.1.4 Conditional Probability ..........................437 A.1.5 Statistical Independence .........................438 A.2 Random Variables .....................................439 A.2.1 Fundamentals ..................................439 A.2.2 Mathematical Expectation .......................441 A. 2.3 Variance and Covariance .........................442 В Signal Processing ..........................................445 B.I Introduction ..........................................445 B.2 The Complex Numbers .................................445 B.3 The ¿-Transform....................................... 447 B.3.1 z-Transform Properties ..........................449 B.3.2 The Fourier Transform ..........................451 B.3.3 The Discrete Fourier Transform ..................452 B.4 The Discrete Cosine Transform ..........................453 С Matrix Algebra ............................................457 C.I Introduction ..........................................457 C.2 Fundamentals .........................................457 C.3 Determinants ..........................................458 C.4 Eigenvalues and Eigenvectors ............................460 D Mathematical Foundations of Kernel Methods .............463 D.I Introduction ..........................................463 D.2 Scalar Products, Norms and Metrics .....................464 D.3 Positive Definite Kernels and Matrices ....................465 D.3.1 How to Make a Mercer Kernel ....................469 D.4 Condiţionate Positive Definite Kernels and Matrices ........471 D.5 Negative Definite Kernels and Matrices ...................472 D.6 Relations Between Positive and Negative Definite Kernels ... 474 D.7 Metric Computation by Mercer Kernels ...................476 D.8 Hubert Space Representation of Positive Definite Kernels ... 478 D.9 Conclusions ...........................................480 XVI Contents References .....................................................481 Index ..........................................................483
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id	DE-604.BV023220446
illustrated	Illustrated
indexdate	2024-12-20T13:10:57Z
institution	BVB
isbn	9781848000063 9781848000070
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-016406357
oclc_num	635181084
open_access_boolean
owner	DE-355 DE-BY-UBR DE-N2
owner_facet	DE-355 DE-BY-UBR DE-N2
physical	XVI, 494 S. Ill., graph. Darst.
publishDate	2008
publishDateSearch	2008
publishDateSort	2008
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 Informatik (DE-588)4026894-9 gnd Videobearbeitung (DE-588)4536854-5 gnd Bildanalyse (DE-588)4145391-8 gnd Digitale Signalverarbeitung (DE-588)4113314-6 gnd Digitales Tonsignal (DE-588)4229078-8 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Maschinelles Sehen (DE-588)4129594-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd
subject_GND	(DE-588)4026894-9 (DE-588)4536854-5 (DE-588)4145391-8 (DE-588)4113314-6 (DE-588)4229078-8 (DE-588)4033447-8 (DE-588)4193754-5 (DE-588)4129594-8 (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	Informatik (DE-588)4026894-9 gnd Videobearbeitung (DE-588)4536854-5 gnd Bildanalyse (DE-588)4145391-8 gnd Digitale Signalverarbeitung (DE-588)4113314-6 gnd Digitales Tonsignal (DE-588)4229078-8 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Maschinelles Sehen (DE-588)4129594-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd
topic_facet	Informatik Videobearbeitung Bildanalyse Digitale Signalverarbeitung Digitales Tonsignal Künstliche Intelligenz Maschinelles Lernen Maschinelles Sehen Bildverarbeitung
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016406357&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT camastrafrancesco machinelearningforaudioimageandvideoanalysistheoryandapplications AT vinciarellialessandro machinelearningforaudioimageandvideoanalysistheoryandapplications

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