Content-Based Image and Video Retrieval / Edition 1by Oge Marques, Borko Furht
The amount of audiovisual information available in digital format has grown exponentially in recent years. Gigabytes of new images, audio and video clips are generated and stored everyday. Most audiovisual content can be accessed through the Internet, which is a very large, unstructured, distributed information database. Searching and retrieving multimedia… See more details below
The amount of audiovisual information available in digital format has grown exponentially in recent years. Gigabytes of new images, audio and video clips are generated and stored everyday. Most audiovisual content can be accessed through the Internet, which is a very large, unstructured, distributed information database. Searching and retrieving multimedia information from the Web has been limited to the use of keywords.
Over the past decade, many researchers, mostly from the Image Processing and Computer Vision community, have started to investigate possible ways of retrieving visual information based solely on its contents. Instead of being manually annotated using keywords, images and video clips would be indexed by their own visual content, such as color, texture, objects' shape and movement, among others. Research in the field of content-based image and video retrieval (CBIVR) is very active. Many research groups in leading universities, research institutes, and companies are actively working in this field. Their ultimate goal is to enable users to retrieve the desired image or video clip among massive amounts of visual data in a fast, efficient, semantically meaningful, friendly, and location-independent manner. Applications of CBIVR systems include digital libraries, video-on-demand systems, geographic information systems, astronomical research, satellite observation systems, and criminal investigation systems, among many others.
Content-Based Image And Video Retrieval addresses the basic concepts and techniques for designing content-based image and video retrieval systems. It also discusses a variety of design choices for the key components of these systems. This book gives a comprehensive survey of the content-based image retrieval systems, including several content-based video retrieval systems. The survey includes both research and commercial content-based retrieval systems. Content-Based Image And Video Retrieval, includes pointers to two hundred representative bibliographic references on this field, ranging from survey papers to descriptions of recent work in the area, entire books and more than seventy websites. Finally, the book presents a detailed case study of designing MUSE–a content-based image retrieval system developed at Florida Atlantic University in Boca Raton, Florida.
Content-Based Image And Video Retrieval is designed to meet the needs of a professional audience composed of researchers, and practitioners in industry and graduate-level students in Computer Science and Engineering.
Table of Contents
|2||Fundamentals of Content-Based Image and Video Retrieval||7|
|3||Designing a Content-Based Image Retrieval System||15|
|4||Designing a Content-Based Video Retrieval System||35|
|5||A Survey of Content-Based Image Retrieval Systems||47|
|6||Case Study: MUSE||103|
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