Semantic-Based Visual Information Retrieval, 1st Edition

  • Published By:
  • ISBN-10: 1599043726
  • ISBN-13: 9781599043722
  • Grade Level Range: College Freshman - College Senior
  • 368 Pages | eBook
  • Original Copyright 2007 | Published/Released December 2007
  • This publication's content originally published in print form: 2007

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Semantic-Based Visual Information Retrieval is one of the most challenging research directions of content-based visual information retrieval. It provides efficient tools for access, interaction, searching, and retrieving from collected databases of visual media. Building on research from over 30 leading experts from around the world, Semantic-Based Visual Information Retrieval presents state-of-the-art advancements and developments in the field, and also brings a selection of techniques and algorithms about semantic-based visual information retrieval. It covers many critical issues, such as: multi-level representation and description, scene understanding, semantic modeling, image and video annotation, human-computer interaction, and more. Semantic-Based Visual Information Retrieval also explains detailed solutions to a wide range of practical applications. Researchers, students, and practitioners will find this comprehensive and detailed volume to be a roadmap for applying suitable methods in semantic-based visual information retrieval.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Semantic-Based Visual Information Retrieval: Table of Contents.
1: Introduction and Background.
2: Toward High-Level Visual Information Retrieval.
3: From Features to Semantics.
4: The Impact of Low-Level Features in Semantic-Based Image Retrieval.
5: Shape-Based Image Retrieval by Alignment.
6: Statistical Audio-Visual Data Fusion for Video Scene Segmentation.
7: Image and Video Annotation.
8: A Novel Framework for Image Categorization and Automatic Annotation.
9: Automatic and Semi-Automatic Techniques for Image Annotation.
10: Adaptive Metadata Generation for Integration of Visual and Semantic Information.
11: Human-Computer Interaction.
12: Interaction Models and Relevance Feedback in Image Retrieval.
13: Semi-Automatic Ground Truth Annotation for Benchmarking of Face Detection in Video.
14: An Ontology-Based Framework for Semantic Image Analysis and Retrieval.
15: Models and Tools for Semantic Retrieval.
16: A Machine Learning-Based Model for Content-Based Image Retrieval.
17: Neural Networks for Content-Based Image Retrieval.
18: Semantic-Based Video Scene Retrieval Using Evolutionary Computing.
19: Miscellaneous Techniques in Applications.
20: Managing Uncertainties in Image Databases.
21: A Hierarchical Classification Technique for Semantics-Based Image Retrieval.
22: Semantic Multimedia Information Analysis for Retrieval Applications.
About the Authors.