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E-grāmata: Intelligent Big Multimedia Databases

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(Univ De Lisboa, Portugal & Inesc-id, Portugal)
  • Formāts: 324 pages
  • Izdošanas datums: 27-May-2015
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • Valoda: eng
  • ISBN-13: 9789814696661
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Intelligent Big Multimedia DatabasesPalielināt
  • Formāts: 324 pages
  • Izdošanas datums: 27-May-2015
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • Valoda: eng
  • ISBN-13: 9789814696661
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Multimedia databases address a growing number of commercially important applications such as media on demand, surveillance systems and medical systems. The book presents essential and relevant techniques and algorithms to develop and implement large multimedia database systems.The traditional relational database model is based on a relational algebra that is an offshoot of first-order logic and of the algebra of sets. The simple relational model is not powerful enough to address multimedia data. Because of this, multimedia databases are categorized into many major areas. Each of these areas are now so extensive that a major understanding of the mathematical core concepts requires the study of different fields such as information retrieval, digital image processing, feature extraction, fractals, machine learning, neuronal networks and high-dimensional indexing. This book unifies the essential concepts and recent algorithms into a single comprehensive volume.
Preface vii
1 Introduction
1(12)
1.1 Intelligent Multimedia Database
1(4)
1.2 Motivation and Goals
5(1)
1.3 Guide to the Reader
6(1)
1.4 Content
7(6)
2 Multimedia Databases
13(22)
2.1 Relational Databases
13(6)
2.1.1 Structured Query Language SQL
15(1)
2.1.2 Symbolical artificial intelligence and relational databases
16(3)
2.2 Media Data
19(9)
2.2.1 Text
19(2)
2.2.2 Graphics and digital images
21(2)
2.2.3 Digital audio and video
23(4)
2.2.4 SQL and multimedia
27(1)
2.2.5 Multimedia extender
27(1)
2.3 Content-Based Multimedia Retrieval
28(7)
2.3.1 Semantic gap and metadata
31(4)
3 Transform Functions
35(56)
3.1 Fourier Transform
35(18)
3.1.1 Continuous Fourier transform
35(2)
3.1.2 Discrete Fourier transform
37(3)
3.1.3 Fast Fourier transform
40(3)
3.1.4 Discrete cosine transform
43(2)
3.1.5 Two dimensional transform
45(8)
3.2 Wavelet Transform
53(25)
3.2.1 Short-term Fourier transform
53(4)
3.2.2 Continuous wavelet transform
57(4)
3.2.3 Discrete wavelet transform
61(9)
3.2.4 Fast wavelet transform
70(6)
3.2.5 Discrete wavelet transform and images
76(2)
3.3 The Karhunen-Loeve Transform
78(9)
3.3.1 The covariance matrix
79(4)
3.3.2 The Karhunen-Loeve transform
83(1)
3.3.3 Principal component analysis
84(3)
3.4 Clustering
87(4)
3.4.1 k-means
88(3)
4 Compression
91(14)
4.1 Lossless Compression
91(5)
4.1.1 Transform encoding
91(2)
4.1.2 Lempel-Ziv
93(1)
4.1.3 Statistical encoding
93(3)
4.2 Lossy Compression
96(9)
4.2.1 Digital images
96(3)
4.2.2 Digital audio signal
99(2)
4.2.3 Digital video
101(4)
5 Feature Extraction
105(28)
5.1 Basic Image Features
105(8)
5.1.1 Color histogram
105(3)
5.1.2 Texture
108(1)
5.1.3 Edge detection
109(2)
5.1.4 Measurement of angle
111(1)
5.1.5 Information and contour
112(1)
5.2 Image Pyramid
113(3)
5.2.1 Scale space
116(1)
5.3 SIFT
116(7)
5.4 GIST
123(1)
5.5 Recognition by Components
123(1)
5.6 Speech
124(3)
5.6.1 Formant frequencies
125(1)
5.6.2 Phonemes
125(2)
5.7 Feature Vector
127(4)
5.7.1 Contours
127(1)
5.7.2 Norm
128(1)
5.7.3 Distance function
128(1)
5.7.4 Data scaling
129(1)
5.7.5 Similarity
130(1)
5.8 Time Series
131(2)
5.8.1 Dynamic time warping
131(1)
5.8.2 Dynamic programming
132(1)
6 Low Dimensional Indexing
133(38)
6.1 Hierarchical Structures
133(5)
6.1.1 Example of a taxonomy
133(1)
6.1.2 Origins of hierarchical structures
134(4)
6.2 Tree
138(9)
6.2.1 Search tree
138(1)
6.2.2 Decoupled search tree
139(1)
6.2.3 B-tree
140(1)
6.2.4 kd-tree
141(6)
6.3 Metric Tree
147(9)
6.3.1 R-tree
147(5)
6.3.2 Construction
152(1)
6.3.3 Variations
152(1)
6.3.4 High-dimensional space
153(3)
6.4 Space Filling Curves
156(13)
6.4.1 Z-ordering
156(5)
6.4.2 Hilbert curve
161(6)
6.4.3 Fractals and the Hausdorff dimension
167(2)
6.5 Conclusion
169(2)
7 Approximative Indexing
171(10)
7.1 Curse of Dimensionality
171(2)
7.2 Approximate Nearest Neighbor
173(1)
7.3 Locality-Sensitive Hashing
173(4)
7.3.1 Binary Locality-sensitive hashing
174(2)
7.3.2 Projection-based LSH
176(1)
7.3.3 Query complexity LSH
176(1)
7.4 Johnson-Lindenstrauss Lemma
177(1)
7.5 Product Quantization
178(2)
7.6 Conclusion
180(1)
8 High Dimensional Indexing
181(34)
8.1 Exact Search
181(1)
8.2 GEMINI
182(16)
8.2.1 1-Lipschitz property
183(2)
8.2.2 Lower bounding approach
185(3)
8.2.3 Projection operators
188(1)
8.2.4 Projection onto one-dimensional subspace
189(5)
8.2.5 LP Norm dependency
194(3)
8.2.6 Limitations
197(1)
8.3 Subspace Tree
198(13)
8.3.1 Subspaces
198(2)
8.3.2 Content-based image retrieval by image pyramid
200(3)
8.3.3 The first principal component
203(2)
8.3.4 Examples
205(2)
8.3.5 Hierarchies
207(1)
8.3.6 Tree isomorphy
208(2)
8.3.7 Requirements
210(1)
8.4 Conclusion
211(4)
9 Dealing with Text Databases
215(24)
9.1 Boolean Queries
215(2)
9.2 Tokenization
217(1)
9.2.1 Low-level tokenization
217(1)
9.2.2 High-level tokenization
218(1)
9.3 Vector Model
218(4)
9.3.1 Term frequency
218(1)
9.3.2 Information
219(1)
9.3.3 Vector representation
220(1)
9.3.4 Random projection
220(2)
9.4 Probabilistic Model
222(9)
9.4.1 Probability theory
222(1)
9.4.2 Bayes's rule
223(1)
9.4.3 Joint distribution
224(2)
9.4.4 Probability ranking principle
226(1)
9.4.5 Binary independence model
226(4)
9.4.6 Stochastic language models
230(1)
9.5 Associative Memory
231(5)
9.5.1 Learning and forgetting
232(1)
9.5.2 Retrieval
233(1)
9.5.3 Analysis
234(1)
9.5.4 Implementation
235(1)
9.6 Applications
236(3)
9.6.1 Inverted index
236(1)
9.6.2 Spell checker
237(2)
10 Statistical Supervised Machine Learning
239(30)
10.1 Statistical Machine Learning
239(2)
10.1.1 Supervised learning
239(1)
10.1.2 Overfitting
240(1)
10.2 Artificial Neuron
241(2)
10.3 Perceptron
243(6)
10.3.1 Gradient descent
245(3)
10.3.2 Stochastic gradient descent
248(1)
10.3.3 Continuous activation functions
248(1)
10.4 Networks with Hidden Nonlinear Layers
249(6)
10.4.1 Backpropagation
250(2)
10.4.2 Radial basis function network
252(2)
10.4.3 Why does a feed-forward networks with hidden nonlinear units work?
254(1)
10.5 Cross-Validation
255(1)
10.6 Support Vector Machine
256(2)
10.6.1 Linear support vector machine
256(1)
10.6.2 Soft margin
257(1)
10.6.3 Kernel machine
257(1)
10.7 Deep Learning
258(11)
10.7.1 Map transformation cascade
258(5)
10.7.2 Relation between deep learning and subspace tree
263(6)
11 Multimodal Fusion
269(6)
11.1 Constrained Hierarchies
269(1)
11.2 Early Fusion
270(1)
11.3 Late Fusion
270(5)
11.3.1 Multimodal fusion and images
271(1)
11.3.2 Stochastic language model approach
271(1)
11.3.3 Dempster-Shafer theory
272(3)
12 Software Architecture
275(6)
12.1 Database Architecture
275(1)
12.1.1 Client-server system
275(1)
12.1.2 A peer-to-peer
276(1)
12.2 Big Data
276(2)
12.2.1 Divide and conquer
276(1)
12.2.2 MapReduce
276(2)
12.3 Evaluation
278(3)
12.3.1 Precision and recall
279(2)
13 Multimedia Databases in Medicine
281(10)
13.1 Medical Standards
281(1)
13.1.1 Health Level Seven
281(1)
13.1.2 DICOM
282(1)
13.1.3 PACS
282(1)
13.2 Electronic Health Record
282(7)
13.2.1 Panoramix
283(6)
13.3 Conclusion
289(2)
Bibliography 291(10)
Index 301
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