Multimedia Data Mining
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Multimedia Data Mining

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Description

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.show more

Product details

  • Hardback | 320 pages
  • 160 x 238 x 22mm | 580.6g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 50 black & white illustrations, 22 black & white tables
  • 1584889667
  • 9781584889663

Table of contents

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 alpha-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.show more

About Zhongfei Zhang

State University of New York, Binghamton, USA Yahoo Inc., Sunnyvale, California, USAshow more

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