Paisarn Muneesawang – författare

Multimedia Database Retrieval: A Human-Centered Approach presents thelatest development in user-centered methods and the state-of-the-art invisual media retrieval.  It includes discussion on perceptually inspirednon-linear paradigm in user-controlled interactive retrieval (UCIR)systems. It also features a coherent approach which focuses on specific topicswithin content/concept-based retrievals via audio-visual information modelingof multimedia.

Highlights include:

* Exploring an adaptive machine that can learn from itsenvironment

* Optimizing the learning system by incorporating self-organizingadaptation into the retrieval process

* Demonstrating state-of-the-art applications within small,medium, and large databases

The authors also include applications related to Digital AssetManagement (DAM), Computer Aided Referral (CAR) System, GeographicalDatabase Retrieval, retrieval of Art Documents, and Films and VideoRetrieval.

Multimedia Database Retrieval: A Human-Centered Approach presents thefundamental and advanced aspects of these topics, as well as thephilosophical directions in the field.  The methods detailed in thisbook possess broad applications which will advance the technology inthis fast developing topical area.

A new approach to unsupervised learning

Evolving technologies have brought about an explosion of information in recent years, but the question of how such information might be effectively harvested, archived, and analyzed remains a monumental challenge—for the processing of such information is often fraught with the need for conceptual interpretation: a relatively simple task for humans, yet an arduous one for computers.

Inspired by the relative success of existing popular research on self-organizing neural networks for data clustering and feature extraction, Unsupervised Learning: A Dynamic Approach presents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM) and the more advanced self-organizing hierarchical variance map (SOHVM). It covers a series of pertinent, real-world applications with regard to the processing of multimedia data—from its role in generic image processing techniques, such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.

Self-organization concepts and applications discussed include:

Unsupervised Learning: A Dynamic Approach introduces a new family of unsupervised algorithms that have a basis in self-organization, making it an invaluable resource for researchers, engineers, and scientists who want to create systems that effectively model oppressive volumes of data with little or no user intervention.

A new approach to unsupervised learning

Evolving technologies have brought about an explosion of information in recent years, but the question of how such information might be effectively harvested, archived, and analyzed remains a monumental challenge—for the processing of such information is often fraught with the need for conceptual interpretation: a relatively simple task for humans, yet an arduous one for computers.

Inspired by the relative success of existing popular research on self-organizing neural networks for data clustering and feature extraction, Unsupervised Learning: A Dynamic Approach presents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM) and the more advanced self-organizing hierarchical variance map (SOHVM). It covers a series of pertinent, real-world applications with regard to the processing of multimedia data—from its role in generic image processing techniques, such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.

Self-organization concepts and applications discussed include:

Unsupervised Learning: A Dynamic Approach introduces a new family of unsupervised algorithms that have a basis in self-organization, making it an invaluable resource for researchers, engineers, and scientists who want to create systems that effectively model oppressive volumes of data with little or no user intervention.

A presentation of the use of computer vision systems to control manufacturing processes and product quality in the hard disk drive industry. Visual Inspection Technology in the Hard Disk Drive Industry is an application-oriented book borne out of collaborative research with the world’s leading hard disk drive companies. It covers the latest developments and important topics in computer vision technology in hard disk drive manufacturing, as well as offering a glimpse of future technologies.

A presentation of the use of computer vision systems to control manufacturing processes and product quality in the hard disk drive industry. Visual Inspection Technology in the Hard Disk Drive Industry is an application-oriented book borne out of collaborative research with the world’s leading hard disk drive companies. It covers the latest developments and important topics in computer vision technology in hard disk drive manufacturing, as well as offering a glimpse of future technologies.