Intelligent big multimedia databases:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New Jersey [u.a.]
World Scientific
2015
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references (pages 291-299) and index |
| Beschreibung: | xvi, 305 S. Ill. 24 cm |
| ISBN: | 9789814696647 |
Internformat
MARC
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| 245 | 1 | 0 | |a Intelligent big multimedia databases |c Andreas Wichert |
| 264 | 1 | |a New Jersey [u.a.] |b World Scientific |c 2015 | |
| 300 | |a xvi, 305 S. |b Ill. |c 24 cm | ||
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| 500 | |a Includes bibliographical references (pages 291-299) and index | ||
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Preface vii
1. Introduction i
1.1 Intelligent. Multimedia Database .......................... 1
1.2 Motivation and Goals ...................................... 5
1.3 Guide to the Reader........................................ 6
1.4 Content ................................................... 7
2. Multimedia Databases 13
2.1 Relational Databases...................................... 13
2.1.1 Structured Query Language SQL ..................... 15
2.1.2 Symbolical artificial intelligence and relational
databases.......................................... 16
2.2 Media Data................................................ 19
2.2.1 Text............................................... 19
2.2.2 Graphics and digital images ....................... 21
2.2.3 Digital audio and video............................ 23
2.2.4 SQL and multimedia................................. 27
2.2.5 Multimedia extender ............................... 27
2.3 Content-Based Multimedia Retrieval........................ 28
2.3.1 Semantic gap and metadata ......................... 31
3. Transform Functions 35
3.1 Fourier Transform ........................................ 35
3.1.1 Continuous Fourier transform ...................... 35
3.1.2 Discrete Fourier transform......................... 37
xi
xii Intelligent Big Multimedia Databases
3.1.3 Fast Fourier transform .........................
3.1.4 Discrete cosine transform.......................
3.1.5 Two dimensional transform.......................... ^
3.2 Wavelet Transform......................................... ^
3.2.1 Short-term Fourier transform....................
3.2.2 Continuous wavelet transform.................... ;
3.2.3 Discrete wavelet transform......................
3.2.4 Fast wavelet transform..........................
3.2.5 Discrete wavelet transform and ilium s............ 75
3.3 The Karhunen-Loeve Transform............................. 7S
3.3.1 The covariance mat rix ........................... 79
3.3.2 The Karlmnon-Loove transform..................... *s3
3.3.3 Principal component analysis.................... *1
3.4 Clustering............................................. St
3.4.1 ¿-means......................................... (S S
4. Compression 11
4.1 Lossless Compression................................... 91
4.1.1 Transform encoding.............................. 91
4.1.2 Lempel-Ziv........................................ 93
4.1.3 Statistical encoding.............................. 93
4.2 Lossy Compression....................................... 9(
4.2.1 Digital images................................... 9(
4.2.2 Digital audio signal............................. ÍK)
4.2.3 Digital video.................................... 101
5. Feature Extraction 105
5.1 Basic Image Features.................................... 105
5.1.1 Color histogram.................................. 105
5.1.2 Texture.......................................... 108
5.1.3 Edge detection................................... 109
5.1.4 Measurement of angle............................. Ill
5.1.5 Information and contour.......................... 112
5.2 Image Pyramid........................................... 113
5.2.1 Scale space....................................... hq
5·3 sin................n«
5-4 GIST ................................................... 123
5.5 Recognition by Components............................... 123
Contents xiü
5.G Speech.................................................. 124
5.6.1 Formant frequencies.............................. 125
5.G.2 Phonemes ........................................ 125
5.7 Feature Vector........................................... 127
5.7.1 Contours......................................... 127
5.7.2 Norm............................................. 128
5.7.5 Distance՝ function............................... 128
5.7.1 Data scaling..................................... 129
5.7.5 Similarity....................................... 130
5.N Time Series............................................. 131
5.rS. 1 Dynamic t iiur warping........................... 131
5.8.2 Dynamic programming.............................. 132
(i. Low Dimensional Indexing 133
(i.l Hierarchical Structures.................................. 133
(i.1.1 Kxample of a taxonomy............................ 133
(i.1.2 Origins of hierarchical structures............... 134
G.2 Tree..................................................... 138
6.2.1 Search tree...................................... 138
0.2.2 Decoupled search tree............................ 139
6.2.3 B-tree........................................... 140
6.2.4 kd-tree.......................................... 141
6.3 Metric Tree.............................................. 147
6.3.1 R-troc........................................... 147
6.3.2 Construction..................................... 152
6.3.3 Variations ...................................... 152
6.3.4 High-dimensional space........................... 153
6.4 Space Filling Curves..................................... 156
6.4.1 Z-ordering....................................... 156
6.4.2 Hilbert curve.................................... 161
6.4.3 Fractals and the Hausdorff dimension............. 167
6.5 Conclusion .............................................. 169
7. Approximative Indexing 171
7.1 Curse of Dimensionality.................................. 171
7.2 Approximate Nearest Neighbor............................. 173
7.3 Locality-Sensitive Hashing............................... 173
7.3.1 Binary Locality-sensitive hashing ............... 174
xiv Intelligent Big Multimedia Databases
7.3.2 Projection-based LSH............................
7.3.3 Query complexity LSH............................
I
7.4 Jolmson-Lindenstrauss Lemma............................. i
I V
7.5 Product Quantization.................................... 1 (
7.6 Conclusion .............................................
8. High Dimensional Indexing
8.1 Exact Search............................................ *։S*
8.2 GEMINI................................................... ls2
8.2.1 1-Lipschitz property............................. hS3
8.2.2 Lower hounding approach ........................ bS;
8.2.3 Projection operators............................. hsx
8.2.4 Projeetion onto one-dimensional subspace........ IS!)
8.2.5 lp norm dependency.............................. I )l
8.2.6 Limitations...................................... Ih7
8.3 Subspace Tree............................................ IbX
8.3.1 Subspaces........................................ E)X
8.3.2 Content-based image retrieval by image pyramid . 200
8.3.3 The first principal component.................... 263
8.3.4 Examples......................................... 205
8.3.5 Hierarchies...................................... 207
8.3.6 Tree isomorphy ...................................208
8.3.7 Requirements .................................... 210
8.4 Conclusion .............................................. 211
9. Dealing with Text Databases 215
9.1 Boolean Queries.......................................... 215
9.2 Tokenization............................................. 217
9.2.1 Low-level tokenization........................... 217
9.2.2 High-level tokenization.......................... 218
9.3 Vector Model............................................. 218
9.3.1 Term frequency....................................218
9.3.2 Information ..................................... 219
9.3.3 Vector representation............................ 220
9.3.4 Random projection.................................220
9.4 Probabilistic Model...................................... 222
9.4.1 Probability theory............................... 222
9.4.2 Bayes’s rule.......................................
Contents
XV
9.4.3 Joint distribution............................... 224
9.4.4 Probability ranking principle.................... 226
9.4.5 Binary independence model ...................... 226
9.4.6 Stochastic language models....................... 230
9.5 Associative՝ Memory...................................... 231
9.5.1 Learning and forgetting.......................... 232
9.5.2 Retrieval........................................ 233
9.5.3 Analysis ........................................ 234
9.5.4 Implementation................................... 235
9.6 Applications............................................. 236
9.6.1 Inverted index................................... 236
9.6.2 Spell checker.................................... 237
10. Statistical Supervised Machine Learning 239
10.1 Statistical Machine՝ Learning ........................... 239
10.1.1 Supervised learning.............................. 239
JO. 1.2 Overfitting...................................... 240
10.2 Artificial Neuron........................................ 241
10.3 Percept ron ............................................. 243
10.3.1 Gradient, descent................................ 245
10.3.2 Stochastic gradient descent...................... 248
10.3.3 Continuous activation functions.................. 248
10.4 Networks with Hidden Nonlinear Layers.................... 249
10.4.1 Baokpropagation.................................. 250
10.4.2 Radial basis function network.................... 252
10.4.3 Why does a feed-forward networks with hidden
nonlinear units work?............................. 254
10.5 Cross-Validation......................................... 255
10.6 Support Vector Machine................................... 256
10.6.1 Linear support vector machine.................... 256
10.6.2 Soft margin ..................................... 257
10.6.3 Kernel machine................................... 257
10.7 Deep Learning ........................................... 258
10.7.1 Map transformation cascade....................... 258
10.7.2 Relation between deep learning and subspace tree 263
11. Multimodal Fusion 259
11.1 Constrained Hierarchies.................................. 269
XVI
Intelligent IS Lg Multimedia I ){l( ulxisi ֊s
11.2 Early Fusion .......................................... ~/U
11.3 Late Fusion........................................... ~,iJ
11.3.1 Multimodal fusion and images.................... 271
11.3.2 Stochastic language model approach.............. 271
11.3.3 Dempster-Shafer theory.......................... 272
12. Software Architecture
12.1
12.2
12.3
Database Architecture . . .
12.1.1 Client-server system
12.1.2 A peer-to-peer . . .
Big Data ..................
12.2.1 Divide՝ and conquer
12.2.2 MapReduce . . . .
Evaluation.................
12.3.1 Precision and recall
27a
27a
271»
2715
2715
2715
27S
13. Multimedia Databases in Medicine 231
13.1 Medical Standards........................................ 231
13.1.1 Health Level Seven ............................. 23 1
13.1.2 DICOM............................................ 282
13.1.3 PACS..............................................232
13.2 Electronic Health Record ................................ 232
13.2.1 Panoramix.........................................283
13.3 Conclusion .............................................. 289
Bibliography
Index
|
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| indexdate | 2024-07-10T07:12:34Z |
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| isbn | 9789814696647 |
| language | English |
| lccn | 015472771 |
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| spelling | Wichert, Andreas Verfasser (DE-588)1081491957 aut Intelligent big multimedia databases Andreas Wichert New Jersey [u.a.] World Scientific 2015 xvi, 305 S. Ill. 24 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (pages 291-299) and index Multimedia (DE-588)4192358-3 gnd rswk-swf Datenbank (DE-588)4011119-2 gnd rswk-swf Datenbanksystem (DE-588)4113276-2 gnd rswk-swf Datenbank (DE-588)4011119-2 s Multimedia (DE-588)4192358-3 s Datenbanksystem (DE-588)4113276-2 s DE-604 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028335767&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Wichert, Andreas Intelligent big multimedia databases Multimedia (DE-588)4192358-3 gnd Datenbank (DE-588)4011119-2 gnd Datenbanksystem (DE-588)4113276-2 gnd |
| subject_GND | (DE-588)4192358-3 (DE-588)4011119-2 (DE-588)4113276-2 |
| title | Intelligent big multimedia databases |
| title_auth | Intelligent big multimedia databases |
| title_exact_search | Intelligent big multimedia databases |
| title_full | Intelligent big multimedia databases Andreas Wichert |
| title_fullStr | Intelligent big multimedia databases Andreas Wichert |
| title_full_unstemmed | Intelligent big multimedia databases Andreas Wichert |
| title_short | Intelligent big multimedia databases |
| title_sort | intelligent big multimedia databases |
| topic | Multimedia (DE-588)4192358-3 gnd Datenbank (DE-588)4011119-2 gnd Datenbanksystem (DE-588)4113276-2 gnd |
| topic_facet | Multimedia Datenbank Datenbanksystem |
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