Zhang Multimedia Data Mining

A Systematic Introduction to Concepts and Theory
1. Auflage 2010
ISBN: 978-1-58488-967-0
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 320 Seiten

Reihe: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Collecting the latest developments in the field, Multimedia Data Mining: A Systematic Introduction to Concepts and Theory defines multimedia data mining, its theory, and its applications. Two of the most active researchers in multimedia data mining explore how this young area has rapidly developed in recent years.

The book first discusses the theoretical foundations of multimedia data mining, presenting commonly used feature representation, knowledge representation, statistical learning, and soft computing techniques. It then provides application examples that showcase the great potential of multimedia data mining technologies. In this part, the authors show how to develop a semantic repository training method and a concept discovery method in an imagery database. They demonstrate how knowledge discovery helps achieve the goal of imagery annotation. The authors also describe an effective solution to large-scale video search, along with an application of audio data classification and categorization.

This novel, self-contained book examines how the merging of multimedia and data mining research can promote the understanding and advance the development of knowledge discovery in multimedia data.

Zielgruppe

Computer scientists and researchers in mathematics, statistics, and computer engineering.

Autoren/Hrsg.

Zhang, Zhongfei

Zhang, Ruofei

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

INTRODUCTION
Introduction
Defining the Area
A Typical Architecture of a Multimedia Data Mining System
The Content and the Organization of This Book
The Audience of This Book
Further Readings
THEORY AND TECHNIQUES
Feature and Knowledge Representation for Multimedia Data
Basic Concepts
Feature Representation
Knowledge Representation
Statistical Mining Theory and Techniques
Bayesian Learning
Probabilistic Latent Semantic Analysis
Latent Dirichlet Allocation for Discrete Data Analysis
Hierarchical Dirichlet Process
Applications in Multimedia Data Mining
Support Vector Machines
Maximum Margin Learning for Structured Output Space
Boosting
Multiple Instance Learning
Semi-Supervised Learning
Soft Computing-Based Theory and Techniques
Characteristics of the Paradigms of Soft Computing
Fuzzy Set Theory
Artificial Neural Networks
Genetic Algorithms
MULTIMEDIA DATA MINING APPLICATION EXAMPLES
Image Database Modeling—Semantic Repository Training
Background
Related Work
Image Features and Visual Dictionaries
a-Semantics Graph and Fuzzy Model for Repositories
Classification-Based Retrieval Algorithm
Experiment Results

Image Database Modeling—Latent Semantic Concept Discovery
Background and Related Work
Region-Based Image Representation
Probabilistic Hidden Semantic Model
Posterior Probability-Based Image Mining and Retrieval
Approach Analysis
Experimental Results
A Multimodal Approach to Image Data Mining and Concept Discovery
Background
Related Work
Probabilistic Semantic Model
Model-Based Image Annotation and Multimodal Image Mining and Retrieval
Experiments
Concept Discovery and Mining in a Video Database
Background
Related Work
Video Categorization
Query Categorization
Experiments
Concept Discovery and Mining in an Audio Database
Background and Related Work
Feature Extraction
Classification Method
Experimental Results
References
Index
An Introduction and Summary appear in each chapter.