Video Object Extraction and Representation: Theory and Applications is an essential reference for electrical engineers working in video; computer scientists researching or building multimedia databases; video system designers; students of video processing; video technicians; and designers working in the graphic arts. In the coming years, the explosion of computer technology will enable a new form of digital media. Along with broadband Internet access and MPEG standards, this new media requires a computational infrastructure to allow users to grab and manipulate content. The book reviews relevant technologies and standards for content-based processing and their interrelations. Within this overview, the book focuses upon two problems at the heart of the algorithmic/computational infrastructure: video object extraction, or how to automatically package raw visual information by content; and video object representation, or how to automatically index and catalogue extracted content for browsing and retrieval. The book analyzes the designs of two novel, working systems for content-based extraction and representation in the support of MPEG-4 and MPEG-7 video standards, respectively. Features of the book include: Overview of MPEG standards; A working system for automatic video object segmentation; A working system for video object query by shape; Novel technology for a wide range of recognition problems; Overview of neural network and vision technologies Video Object Extraction and Representation: Theory and Applications will be of interest to research scientists and practitioners working in fields related to the topic. It may also be used as an advanced-level graduate text.

Video Object Extraction and Representation: Theory and Applications is an essential reference for electrical engineers working in video; computer scientists researching or building multimedia databases; video system designers; students of video processing; video technicians; and designers working in the graphic arts. In the coming years, the explosion of computer technology will enable a new form of digital media. Along with broadband Internet access and MPEG standards, this new media requires a computational infrastructure to allow users to grab and manipulate content. The book reviews relevant technologies and standards for content-based processing and their interrelations. Within this overview, the book focuses upon two problems at the heart of the algorithmic/computational infrastructure: video object extraction, or how to automatically package raw visual information by content; and video object representation, or how to automatically index and catalogue extracted content for browsing and retrieval.The book analyzes the designs of two novel, working systems for content-based extraction and representation in the support of MPEG-4 and MPEG-7 video standards, respectively. Features of the book include: * Overview of MPEG standards; * A working system for automatic video object segmentation; * A working system for video object query by shape; * Novel technology for a wide range of recognition problems; * Overview of neural network and vision technologies Video Object Extraction and Representation: Theory and Applications will be of interest to research scientists and practitioners working in fields related to the topic. It may also be used as an advanced-level graduate text.

Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.