Overview

""Exploration of Visual Data"" presents 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 colour, 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low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.

Full Product Details

Author:   Sean Xiang Zhou ,  Yong Rui ,  Thomas S. Huang
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2003 ed.
Volume:   7
Dimensions:   Width: 15.50cm , Height: 1.20cm , Length: 23.50cm
Weight:   1.050kg
ISBN:  

9781402075698

ISBN 10:   1402075693
Pages:   187
Publication Date:   30 September 2003
Audience:   General/trade ,  Professional and scholarly ,  General ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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.

Reviews

Author Information

Tab Content 6

Author Website:  

Customer Reviews

Recent Reviews

No review item found!

Add your own review!

Countries Available

All regions