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9781402075698

Exploration of Visual Data

by ; ;
  • ISBN13:

    9781402075698

  • ISBN10:

    1402075693

  • Format: Hardcover
  • Copyright: 2003-08-01
  • Publisher: Kluwer Academic Pub

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Supplemental Materials

What is included with this book?

Summary

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low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.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

Author Biography

Xiang Sean Zhou: Siemens Corporation Princeton, NJ, U.S.A. Yong Rui Microsoft Research Redmond, WA, U.S.A. Thomas S. Huang University of Illinois at Urbana-Champaign Urbana, IL, U.S.A.

Table of Contents

1. INTRODUCTION 1(4)
1.1 Challenges
1(1)
1.2 Research Scope
2(1)
1.3 State-of-the-Art
2(2)
1.3.1 Visual information extraction and representation
2(1)
1.3.2 Learning from user interactions
3(1)
1.3.3 Temporal analysis and segmentation of video
3(1)
1.3.4 Content-sensitive low bit-rate video streaming
3(1)
1.4 Outline of Book
4(1)
2. OVERVIEW OF VISUAL INFORMATION REPRESENTATION 5(10)
2.1 Color
6(1)
2.2 Texture
7(1)
2.3 Shape
8(2)
2.4 Spatial Layout
10(1)
2.5 Interest Points
11(1)
2.6 Image Segmentation
12(1)
2.7 Summary
13(2)
3. EDGE-BASED STRUCTURAL FEATURES 15(24)
3.1 Visual Feature Representation
15(2)
3.1.1 The scope of our research
16(1)
3.1.2 Background
16(1)
3.1.3 The quest for structure
16(1)
3.2 Edge-Based Structural Features
17(7)
3.2.1 The water-filling algorithm
18(2)
3.2.2 Edge feature extraction
20(4)
3.2.3 Scale invariance and cross-scale matching
24(1)
3.3 Experiments and Analysis
24(15)
3.3.1 City/building and landscape images
25(2)
3.3.2 Images with clear structure: Birds and airplanes in the sky
27(2)
3.3.3 High-level concepts: Horses, tigers, and cars
29(6)
3.3.4 Medical image retrieval
35(1)
3.3.5 Cross-scale image matching
35(1)
3.3.6 when will it fail?-Bad examples
36(3)
4. PROBABILISTIC LOCAL STRUCTURE MODELS 39(14)
4.1 Introduction
39(1)
4.2 The Proposed Modeling Scheme
40(4)
4.2.1 Classification by class-conditional density
41(1)
4.2.2 Joint distribution for k-tuples
41(1)
4.2.3 Histogram factorization based on ICA
42(1)
4.2.4 Distance-sensitive histograming for modeling spatial dependencies
43(1)
4.3 Implementation Issues
44(1)
4.4 Experiments and Discussion
44(7)
4.4.1 Object detection/localization
44(5)
4.4.2 Image retrieval
49(2)
4.5 Summary and Discussion
51(2)
5. CONSTRUCTING TABLE-OF-CONTENT FOR VIDEOS 53(22)
5.1 Introduction
53(3)
5.2 Related Work
56(2)
5.2.1 Shot and key frame based video ToC
56(1)
5.2.2 Group based video ToC
57(1)
5.2.3 Scene based video ToC
57(1)
5.3 The Proposed Approach
58(9)
5.3.1 Shot Boundary Detection and Key Frame Extraction
59(1)
5.3.2 Spatio-Temporal Feature Extraction
59(1)
5.3.3 Time-Adaptive Grouping
60(2)
5.3.4 Scene Structure Construction
62(5)
5.4 Determination of the Parameters
67(3)
5.4.1 Gaussian Normalization
68(1)
5.4.2 Determining W and WA
69(1)
5.4.3 Determining groupThreshold and sceneThreshold
69(1)
5.5 Experimental Results
70(2)
5.6 Conclusions
72(3)
6. NONLINEARLY SAMPLED VIDEO STREAMING 75(22)
6.1 Introduction
76(2)
6.2 Problem Statement
78(1)
6.3 Frame Saliency Scoring
79(1)
6.4 Scenario and Assumptions
80(1)
6.5 Minimum Buffer Formulation
81(4)
6.5.1 As an integer optimization problem
82(1)
6.5.2 As a shortest path problem
82(1)
6.5.3 Dynamic programming solution
83(2)
6.6 Limited-Buffer Formulation
85(2)
6.6.1 Channel and buffer modeling by Z-B diagram
85(1)
6.6.2 The algorithm
86(1)
6.6.3 Greedy strategy will fail
87(1)
6.7 Extensions and Analysis
87(5)
6.7.1 User-in-the-loop: Interactive frame selection
87(2)
6.7.2 Variable file size
89(1)
6.7.3 Time-varying bandwidth
90(1)
6.7.4 Key-segments versus key-frames
91(1)
6.7.5 Tolerance for small delays
91(1)
6.7.6 Complexity analysis
92(1)
6.8 Experimental Evaluation
92(2)
6.9 Discussion
94(3)
7. RELEVANCE FEEDBACK FOR VISUAL DATA RETRIEVAL 97(52)
7.1 The Need for User-in-the-Loop
98(1)
7.2 Problem Statement
99(1)
7.3 Overview of Existing Techniques
100(4)
7.3.1 Variants
100(2)
7.3.2 From heuristic to optimal scheme
102(2)
7.4 Learning from Positive Feedbacks
104(15)
7.4.1 Notations
105(2)
7.4.2 MARS and MindReader approaches
107(2)
7.4.3 A hierarchical optimization approach
109(4)
7.4.4 Experiments and Evaluations
113(6)
7.4.5 Discussions
119(1)
7.5 Adding Negative Feedbacks: Discriminant Analysis?
119(3)
7.5.1 Two-class assumption
120(1)
7.5.2 Multiclass assumption
121(1)
7.5.3 Unsupervised clustering
121(1)
7.5.4 Dimensionality reduction matrix
122(1)
7.6 Biased Discriminant Analysis
122(5)
7.6.1 (1+x)-class assumption
122(1)
7.6.2 Biased discriminant analysis (BDA)
123(1)
7.6.3 Generalized BDA
123(1)
7.6.4 Regularization and discounting factors
124(1)
7.6.5 Discriminating transform
125(1)
7.6.6 Properties of the discriminating transform
125(2)
7.7 Nonlinear Extensions Using Kernel and Boosting
127(8)
7.7.1 Boosting biased discriminant analysis (BBDA)
127(2)
7.7.2 Biased discriminant analysis using kernel (KBDA)
129(6)
7.8 Comparisons and Analysis
135(10)
7.8.1 Linear/quadratic case
136(4)
7.8.2 Nonlinear case using kernel
140(3)
7.8.3 Nonlinear case using boosting
143(2)
7.9 Relevance Feedback on Image Tiles
145(4)
8. TOWARD UNIFICATION OF KEYWORDS AND LOWLEVEL CONTENTS 149(14)
8.1 Introduction
150(3)
8.1.1 Automatic thesaurus construction in document analysis
150(1)
8.1.2 Outline of proposed method
151(1)
8.1.3 Background and assumptions
151(2)
8.2 Joint Querying and Relevance Feedback
153(3)
8.2.1 Soft vector representation of keywords
153(1)
8.2.2 Joint global similarity search
154(2)
8.3 Learning Semantic Relations between Keywords
156(6)
8.3.1 WARF: Word Association via Relevance Feedback
157(2)
8.3.2 Semantic grouping of keywords
159(3)
8.4 Discussion
162(1)
9. FUTURE RESEARCH DIRECTIONS 163(4)
9.1 Low-level and intermediate-level visual descriptors
163(1)
9.2 Learning from user interactions
164(1)
9.3 Unsupervised detection of patterns/events
164(1)
9.4 Domain-specific applications
164(3)
REFERENCES 167(18)
INDEX 185

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