Sean Xiang Zhou; Yong Rui; Thomas S. Huang Exploration of Visual Data 9781461351061

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Автор: Sean Xiang Zhou; Yong Rui; Thomas S. Huang
Название: Exploration of Visual Data
ISBN: 9781461351061
Издательство: Springer
Классификация:

Графические и цифровые медиа-приложения

Алгоритмы и структуры данных

Искусственный интеллект

Машинное зрение

Обработка изображений

ISBN-10: 1461351065
Обложка/Формат: Paperback
Страницы: 187
Вес: 0.30 кг.
Дата издания: 29.10.2012
Серия: The International Series in Video Computing
Язык: English
Издание: Softcover reprint of
Иллюстрации: Xvii, 187 p.
Размер: 234 x 156 x 11
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание:

Exploration of Visual Data presents latest research efforts in the area of content-based exploration of image and video data. The main objective is to bridge the semantic gap between high-level concepts in the human mind and low-level features extractable by the machines.

The two key issues emphasized are content-awareness and user-in-the-loop. The authors provide a comprehensive review on algorithms for visual feature extraction based on color, texture, shape, and structure, and techniques for incorporating such information to aid browsing, exploration, search, and streaming of image and video data. They also discuss issues related to the mixed use of textual and low-level visual features to facilitate more effective access of multimedia data.

To bridge the semantic gap, significant recent research efforts have also been put on learning during user interactions, which is also known as relevance feedback. The difficulty and challenge also come from the personalized information need of each user and a small amount of feedbacks the machine could obtain through real-time user interaction. The authors present and discuss several recently proposed classification and learning techniques that are specifically designed for this problem, with kernel- and boosting-based approaches for nonlinear extensions.

Exploration of Visual Data provides state-of-the-art materials on the topics of content-based description of visual data, content-based low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.

Exploration of Visual Data will be of interest to researchers, practitioners, and graduate-level students in the areas of multimedia information systems, multimedia databases, computer vision, machine learning.

Exploration of Visual Data, Sean Xiang Zhou; Yong Rui; Thomas S. Huang