Exploration of Visual Data (Hardcover, 2003 ed.)

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low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.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 theareas of multimedia information systems, multimedia databases, computer vision, machine learning.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	The International Series in Video Computing, 7
Release date:	September 2003
First published:	2003
Authors:	Sean Xiang Zhou • Yong Rui • Thomas S. Huang
Dimensions:	235 x 155 x 12mm (L x W x T)
Format:	Hardcover
Pages:	187
Edition:	2003 ed.
ISBN-13:	978-1-4020-7569-8
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision
LSN:	1-4020-7569-3
Barcode:	9781402075698

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