Multimedia Data Mining: A Systematic Introduction to Concepts and Theory (Hardcover)

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 α-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.