Machine Learning for Multimedia Content Analysis

Capa
Springer Science & Business Media, 26 de set. de 2007 - 277 páginas

Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly.

Machine Learning for Multimedia Content Analysis introduces machine learning techniques that are particularly powerful and effective for modeling spatial, temporal structures of multimedia data and for accomplishing common tasks of multimedia content analysis. This book systematically covers these techniques in an intuitive fashion and demonstrates their applications through case studies. This volume uses a large number of figures to illustrate and visualize complex concepts, and provides insights into the characteristics of many algorithms through examinations of their loss functions and straightforward comparisons.

Machine Learning for Multimedia Content Analysis is designed for an academic and professional audience. Researchers will find this book an invaluable tool for applying machine learning techniques to multimedia content analysis. This volume is also suitable for practitioners in industry.

 

Páginas selecionadas

Conteúdo

Introduction
1
Dimension Reduction 15
13
Data Clustering Techniques
37
Introduction of Graphical Models 73
72
Markov Chains and Monte Carlo Simulation
81
Markov Random Fields and Gibbs Sampling
115
Hidden Markov Models
149
Inference and Learning for General Graphical Models 179
178
Maximum Entropy Model and Conditional
199
MaxMargin Classifications
235
A Appendix
267
Index
275
Direitos autorais

Outras edições - Ver todos

Machine Learning for Multimedia Content Analysis
Yihong Gong,Wei Xu
Prévia não disponível - 2010
Machine Learning for Multimedia Content Analysis
Yihong Gong,Wei Xu
Prévia não disponível - 2007
Machine Learning for Multimedia Content Analysis
Yihong Gong,Wei Xu
Prévia não disponível - 2007

Termos e frases comuns

annealing apply arg max camera shot classifier cliques complex components conditional random field constraints corresponding criterion function data clustering data point defined denote derived directed graphical discriminative models document dual eigenvalues eigenvectors equation estimation example feature functions feature space feature vector foreground object Gaussian Gibbs distribution Gibbs sampling graphical models Hidden Markov Models home run hyperplane independent inference input iterations K-means kernel label Lagrange multiplier linear loss function machine learning machine learning techniques margin Markov chain Markov random field maximizing maximum entropy model MCMC minimize multimedia features non-negative Normalized Cut obtain optimization problem parameters pixels probability distribution random variables rejection sampling respectively simulation solution spectral clustering spectral clustering techniques stationary distribution statistical subspace Theorem training data transition matrix transition probability types of highlights undirected graphical models values weight zero

Informações bibliográficas

TítuloMachine Learning for Multimedia Content Analysis
Multimedia Systems and Applications
Computer Science
Computer Science (R0)
AutoresYihong Gong, Wei Xu
Ediçãoilustrada
EditoraSpringer Science & Business Media, 2007
ISBN0387699422, 9780387699424
Num. págs.277 páginas
  
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