Semantic-Based Visual Information Retrieval

Yu-Jin ZHANG (Editor)

IRM Press, USA£¬2007


Copyright @2007 by Idea Group Inc.

ISBN 1-59904-370-X (hardcover, US$ 94.95)
ISBN 1-59904-371-8 (soft-cover, US$ 79.95)
ISBN 1-59904-372-6 (ebook, US$ 63.96, IGI Web Site only)

386 pages     More information-1  More information-2

Introduction

 (Selected from "Preface")

Content-based visual information retrieval (CBVIR) is one of the most interesting research topics in the last years for image and video community. With the progress of electronic equipments and computer techniques for visual information capturing and processing, a huge number of image and video records have been collected. Visual information becomes a well-known information format and a popular element in all aspects of our society. The large visual data make the dynamic research to be focused on the problem of how to efficiently capture, store, access, process, represent, describe, query, search, and retrieve their contents. In the last years, this field has experienced significant growth and progress, resulting in a virtual explosion of published information.

The research on CBVIR has already a history of more than a dozen years. It has been started by using low-level features such as color, texture, shape, structure and space relationship, as well as (global and local) motion to represent the information content. Research on feature-based visual information retrieval has made quite a bit but limited success. Due to the considerable difference between the users' concerts on the semantic meaning and the appearances described by the above low-level features, the problem of semantic gap arises. One has to shift the research toward some high levels, and especially the semantic level. So, semantic-based visual information retrieval (CBVIR) begins in few years¡¯ ago and soon becomes a notable theme of CBVIR.

How to bridge the gap between semantic meaning and perceptual feeling, which also exists between man and computer, has attracted much attention. Many efforts have been converged to SBVIR in recent years, though it is still in its commencement. As a consequence, there is a considerable requirement for books like this one, which attempts to make a summary of the past progresses and to bring together a broad selection of the latest results from researchers involved in state-of-the-art work on semantic-based visual information retrieval.

This book is intended for scientists and engineers who are engaged in research and development of visual information (especially image and video content) techniques and who wish to keep their paces with the advances of this field. The objective of this collection is to review and survey new forward-thinking research and development in intelligent content-based retrieval technologies. A comprehensive coverage of various branches of semantic-based visual information retrieval is provided by more than 30 leading experts around the world.

Contents

 http://www.loc.gov/catdir/toc/ecip071/2006027731.html

Section I    Introduction
 Chapter 1   Toward High-Level Visual Information Retrieval

Section II   From Features to Semantics
 Chapter 2   The Impact of Low-level Features in Semantic-based Image Retrieval
 Chapter 3   Shape Based Image Retrieval by Alignment
 Chapter 4   Statistical Audio-Visual Data Fusion for Video Scene Segmentation

Section III   Image and Video Annotation
 Chapter 5   A Novel Framework for Image Categorization and Automatic Annotation
 Chapter 6   Automatic and Semi-automatic Techniques for Image Annotation
 Chapter 7   Adaptive Metadata Generation for Integration of Visual and Semantic Information

Section IV   Human-Computer Interaction
 Chapter 8   Interaction Models and Relevance Feedback in Image Retrieval
 Chapter 9   Semi-Automatic Ground Truth Annotation for Benchmarking of Face Detection in Video
 Chapter 10   An Ontology-Based Framework for Semantic Image Analysis and Retrieval

Section V   Models and Tools for Semantic Retrieval
 Chapter 11   A Machine Learning Based Model For Content Based Image Retrieval
 Chapter 12   Neural Networks for Content Based Image Retrieval
 Chapter 13   Semantic-Based Video Scene Retrieval Using Evolutionary Computing

Section VI   Miscellaneous Techniques in Applications
 Chapter 14   Managing Uncertainties in Image Databases
 Chapter 15   A Hierarchical Classification Technique for Semantics-Based Image Retrieval
 Chapter 16   Semantic Multimedia Information Analysis for Retrieval Applications

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