Exploration of Visual Data

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.