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Cognitive visual informatics is an interdisciplinary study of how humans organize, seek, store, and retrieve visual information. The practice of cognitive visual informatics research requires sophisticated theories, information technologies, and neurophysiological methods to design a natural user interface, manage information, and provide customized information services to users. The book introduces theories and technologies that are applied to model and process visual information for this emerging field at the intersection of information science, library science, computer science, and cognitive neuroscience, which combines the research techniques of cognitive psychology with neuroscience techniques,such as electroencephalography/event-related potential (EEG/ERP), to assess the structure and function of the brain. With the rise of cognitive neuroscience, many people with no previous experience in electrophysiology began setting up their own ERP labs. This was an important trend, because these researchers brought considerable expertise from other areas of science and began applying ERPs to a broader range of issues. I was fortunate to be involved in EEG/ERP projects, which were supported by the National Research Foundation of Korea. My goal in writing this book was to summarize research results on cognitive visual informatics that were obtained from the projects. The book seems to be different from other information science books in terms of applying neuroscience, and cognitive science to information science field. The volume is organized so as first to explain basic concepts and then to illustrate them with examples and case studies. Hence, the book is useful for researchers, students, and practitioners of information science and its areas of application who want to know the new trends and applications in this field.

1. Introduction

2. Theories
2.1 Relevance
2.2 Theoretical Framework for Visual Information Recognition and Processing
2.2.1 Uni-modal
2.2.2 Multi-modal
2.3 Narrative Theory

3. Research Method: Focusing on NP Measures
3.1 EEG
3.2 fMRI
3.3 Eye Tracking
3.4 Understanding Topical Relevance Judgment in Visual Simple Search: An EEG Study
3.4.1 Introduction
3.4.2 Hypotheses
3.4.3 Experiments
3.4.4 EEG Recording and Processing
3.4.5 Results

4. Cognitive Models for Multimedia Comprehension
4.1 Baddeley’s WM
4.2 Mayer Model
4.3 Relevance Model
4.3.1 Simple Search Model
4.3.2 Complex Search Model

5. Metadata Framework for Efficient Browsing and Searching of Web Videos
5.1 Introduction
5.2 Multimedia Metadata: A Comparison of PB-Core, TVA, and MPEG-7
5.3 Related Studies
5.4 Structural and Semantic Metadata Framework
5.4.1 Theoretical Model
5.4.2 Metadata Framework
5.5 How to Obtain Metadata Information
5.6 Conclusion

6. Speech Summarization
6.1 Related Studies
6.1.1 Supervised Summarization
6.1.2 Unsupervised Summarization
6.1.3 Social Summarization
6.2 Social Summarization
6.2.1 Tag-based Framework
6.2.2 The Process of Generating Tag-based Summaries
6.3 Latent Semantic Analysis (LSA)
6.3.1 Overview
6.3.2 The Process of Generating LSA-based Summaries
6.4 Maximum Marginal Relevance (MMR)
6.4.1 MMR Model
6.4.2 The Process of Generating MMR-based Summaries
6.5 Acoustic Method
6.5.1 Overview
6.5.2 The Process of Generating Acoustic-based Summaries

7. Video Summarization
7.1 Overview
7.2 Related Studies
7.3 Algorithm for Key-frame Extraction
7.3.1 Two-step Approach to Key-frame Extraction
7.3.2 Example of Storyboard Construction
7.4 Video Summarization Using EEG/ERP Techniques
7.4.1 Overview
7.4.2 Theoretical Background
7.4.3 Research Hypotheses: Semantic Mismatch and Integration
7.4.4 Experimental Methodology
7.4.5 Results

8. Social Information Retrieval
8.1 Three Categories of Social Information Retrieval
8.1.1 Social Indexing
8.1.2 Social Search
8.1.3 Social Recommendation
8.2 Case Studies: Social Indexing and Search
8.2.1 Overview
8.2.2 Social Indexing of Videos
8.2.3 Social Search of Videos

9. Evaluation
9.1 Multimedia Retrieval: Approach and Performance Evaluation
9.2 The Evaluation of Social Summaries Created by the Social Summarization Method
9.2.1 Method
9.2.2 Results
9.2.3 Discussion
9.3 The Evaluation of EEG-based Key Shot Extraction Algorithm
9.3.1 Discriminant Analysis: Variable Selection and Evaluation
9.3.2 Intrinsic Evaluation

Index