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Exploration of Visual Data (Kluwer International Series in Video Computing)

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Exploration of Visual Data (Kluwer International Series in Video Computing) Cover

 

Synopses & Reviews

Publisher Comments:

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low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.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.

Book News Annotation:

This work provides detailed descriptions of recent advances in the area of image and video data exploration, for researchers, practitioners, and graduate students in the areas of multimedia information systems, multimedia databases, computer vision, and machine learning. New methods and algorithms are presented for learning from real-time user interactions, visual object/pattern extraction and representation, temporal video segmentation, content- aware low-bit-rate video streaming, and combined learning in a joint textual and visual domain. The author works in the private sector. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com)

Table of Contents

1: Introduction. 1.1. Challenges. 1.2. Research Scope. 1.3. State-of-the-Art. 1.4. Outline of Book. 2: Overview Of Visual Information Representation. 2.1. Color. 2.2. Texture. 2.3. Shape. 2.4. Spatial Layout. 2.5. Interest Points. 2.6. Image Segmentation. 2.7. Summary. 3: Edge-based Structural Features. 3.1. Visual Feature Representation. 3.2. Edge-Based Structural Features. 3.3. Experiments and Analysis. 4: Probabilistic Local Structure Models. 4.1. Introduction. 4.2. The Proposed Modeling Scheme. 4.3. Implementation Issues. 4.4. Experiments and Discussion. 4.5. Summary and Discussion. 5: Constructing Table-of-Content for Videos. 5.1. Introduction. 5.2. Related Work. 5.3. The Proposed Approach. 5.4. Determination of the Parameters. 5.5. Experimental Results. 5.6. Conclusions. 6: Nonlinearly Sampled Video Streaming. 6.1. Introduction. 6.2. Problem Statement. 6.3. Frame Saliency Scoring. 6.4. Scenario and Assumptions. 6.5. Minimum Buffer Formulation. 6.6. Limited-Buffer Formulation. 6.7. Extensions and Analysis. 6.8. Experimental Evaluation. 6.9. Discussion. 7: Relevance Feedback for Visual Data Retrieval. 7.1. The Need for User-in-the-Loop. 7.2. Problem Statement. 7.3. Overview of Existing Techniques. 7.4. Learning from Positive Feedbacks. 7.5. Adding Negative Feedbacks: Discriminant Analysis? 7.6. Biased Discriminant Analysis. 7.7. Nonlinear Extensions Using Kernel and Boosting. 7.8. Comparisons and Analysis. 7.9. Relevance Feedback on Image Tiles. 8: Toward Unification of Keywords and Low-Level Contents. 8.1. Introduction. 8.2. Joint Querying and Relevance Feedback. 8.3. Learning Semantic Relations between Keywords. 8.4. Discussion. 9: Future Research Directions. 9.1. Low-level and intermediate-level visual descriptors. 9.2. Learning from user interactions. 9.3. Unsupervised detection of patterns/events. 9.4. Domain-specific applications. References. Index.

Product Details

ISBN:
9781402075698
Author:
Zhou, Xiang Sean
Publisher:
Springer
Author:
Huang, Tho
Author:
Huang, Thomas S.
Author:
mas S.
Author:
Xiang Zhou, Sean
Author:
Rui, Yong
Author:
Sean Xiang Zhou
Location:
Boston
Subject:
General
Subject:
Automation
Subject:
Computer Graphics - General
Subject:
Computer graphics
Subject:
Computer Vision
Subject:
Visualization
Subject:
Artificial Intelligence - General
Subject:
Intelligence (AI) & Semantics
Subject:
Computer Imaging, Vision, Pattern Recognition and Graphics
Subject:
Image Processing and Computer Vision
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
multimedia information systems
Subject:
Data Structures, Cryptology and Information Theory
Subject:
Science Reference-Technology
Copyright:
Edition Number:
1
Edition Description:
Book
Series:
Kluwer International Series in Video Computing
Series Volume:
7
Publication Date:
20030930
Binding:
HARDCOVER
Language:
English
Illustrations:
Yes
Pages:
204
Dimensions:
235 x 155 mm 1050 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Database » MIS
Computers and Internet » Internet » Information
Health and Self-Help » Health and Medicine » General
Health and Self-Help » Health and Medicine » General Medicine
Health and Self-Help » Health and Medicine » Medical Specialties
Reference » Science Reference » Technology
Science and Mathematics » Electricity » General Electronics

Exploration of Visual Data (Kluwer International Series in Video Computing) New Hardcover
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