Reinforces basic principles for the development of tools and systems in the field
Offers an application-oriented view of multimedia technology with a balanced perspective on multimedia processing
Reviews the key recent research in all aspects of multimedia including metadata, image, video, and audio
This book explores multimedia applications that emerged from computer vision and machine learning technologies. These state-of-the-art applications include MPEG-7, interactive multimedia retrieval, multimodal fusion, annotation, and database re-ranking. The application-oriented approach maximizes reader understanding of this complex field. Established researchers explain the latest developments in multimedia database technology and offer a glimpse of future technologies. The authors emphasize the crucial role of innovation, inspiring users to develop new applications in multimedia technologies such as mobile media, large scale image and video databases, news video and film, forensic image databases and gesture databases. With a strong focus on industrial applications along with an overview of research topics, Multimedia Database Retrieval: Technology and Applications is an indispensable guide for computer scientists, engineers and practitioners involved in the development and use of multimedia systems. It also serves as a secondary text or reference for advanced-level students interested in multimedia technologies.
Introduction.- Kernel-Based Adaptive Image Retrieval Methods.- Self-Adaptation in Image and Video Retrieval.- Interactive Mobile Visual Search and Recommendation at Internet Scale.- Mobile Landmark Recognition.- Image Retrieval from a Forensic Cartridge Case Database.- Indexing, Object Segmentation, and Event Detection in News and Sports Videos.- Adaptive Retrieval in a P2P Cloud Datacenter.- Scalable Video Genre Classification and Event Detection.- Audio-Visual Fusion for Film Database Retrieval and Classification.- Motion Database Retrieval with Application to Gesture Recognition in a Virtual Realty Dance Training System.
This book explores multimedia applications that emerged from computer vision and machine learning technologies. These state-of-the-art applications include MPEG-7, interactive multimedia retrieval, multimodal fusion, annotation, and database re-ranking. The application-oriented approach maximizes reader understanding of this complex field. Established researchers explain the latest developments in multimedia database technology and offer a glimpse of future technologies. The authors emphasize the crucial role of innovation, inspiring users to develop new applications in multimedia technologies such as mobile media, large scale image and video databases, news video and film, forensic image databases and gesture databases. With a strong focus on industrial applications along with an overview of research topics, Multimedia Database Retrieval: Technology and Applications is an indispensable guide for computer scientists, engineers and practitioners involved in the development and use ofmultimedia systems. It also serves as a secondary text or reference for advanced-level students interested in multimedia technologies.
Introduction.- Kernel-Based Adaptive Image Retrieval Methods.- Self-Adaptation in Image and Video Retrieval.- Interactive Mobile Visual Search and Recommendation at Internet Scale.- Mobile Landmark Recognition.- Image Retrieval from a Forensic Cartridge Case Database.- Indexing, Object Segmentation, and Event Detection in News and Sports Videos.- Adaptive Retrieval in a P2P Cloud Datacenter.- Scalable Video Genre Classification and Event Detection.- Audio-Visual Fusion for Film Database Retrieval and Classification.- Motion Database Retrieval with Application to Gesture Recognition in a Virtual Realty Dance Training System.
Ning Zhang, geb. Jahr 1977, Diplom-Kaufmann, BWL-Studium an der Universität Duisburg-Essen, Campus Essen. Abschluss 2008 als Diplom-Kaufmann. Derzeit tätig als Financial Analyst im Bereich von High Technologies.
Inhaltsverzeichnis
Introduction.- Kernel-Based Adaptive Image Retrieval Methods.- Self-Adaptation in Image and Video Retrieval.- Interactive Mobile Visual Search and Recommendation at Internet Scale.- Mobile Landmark Recognition.- Image Retrieval from a Forensic Cartridge Case Database.- Indexing, Object Segmentation, and Event Detection in News and Sports Videos.- Adaptive Retrieval in a P2P Cloud Datacenter.- Scalable Video Genre Classification and Event Detection.- Audio-Visual Fusion for Film Database Retrieval and Classification.- Motion Database Retrieval with Application to Gesture Recognition in a Virtual Realty Dance Training System.
Klappentext
This book explores multimedia applications that emerged from computer vision and machine learning technologies. These state-of-the-art applications include MPEG-7, interactive multimedia retrieval, multimodal fusion, annotation, and database re-ranking. The application-oriented approach maximizes reader understanding of this complex field. Established researchers explain the latest developments in multimedia database technology and offer a glimpse of future technologies. The authors emphasize the crucial role of innovation, inspiring users to develop new applications in multimedia technologies such as mobile media, large scale image and video databases, news video and film, forensic image databases and gesture databases. With a strong focus on industrial applications along with an overview of research topics, Multimedia Database Retrieval: Technology and Applications is an indispensable guide for computer scientists, engineers and practitioners involved in the development and use ofmultimedia systems. It also serves as a secondary text or reference for advanced-level students interested in multimedia technologies.
Reinforces basic principles for the development of tools and systems in the field
Offers an application-oriented view of multimedia technology with a balanced perspective on multimedia processing
Reviews the key recent research in all aspects of multimedia including metadata, image, video, and audio
Includes supplementary material: sn.pub/extras
