Machine Learning and Statistical Modeling Approaches to Image Retrieval
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Yixin Chen, Jia Li, James Z. Wang
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Machine Learning and Statistical Modeling Approaches to Image Retrieval
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Springer-Verlag
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9781402080357
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1
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CHF 70.40
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Sonstiges
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English
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198
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DRM
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PC/MAC/eReader/Tablet
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PDF
In the early 1990s, the establishment of the Internet brought forth a revolutionary viewpoint of information storage, distribution, and processing: the World Wide Web is becoming an enormous and expanding distributed digital library. Along with the development of the Web, image indexing and retrieval have grown into research areas sharing a vision of intelligent agents. Far beyond Web searching, image indexing and retrieval can potentially be applied to many other areas, including biomedicine, space science, biometric identification, digital libraries, the military, education, commerce, culture and entertainment.
"Machin Learning and Statistical Modeling Approaches to Image Retrieval" describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results. The topics of this book reflect authors' experiences of machine learning and statistical modeling based image indexing and retrieval. This book contains detailed references for further reading and research in this field as well.
2.1 Similarity Comparison
(p.16-17)
Similarity comparison is a key issue in CBIR [Santini and Jain, 1999]. In general, the comparison is performed over imagery features. According to the scope of representation, features fall roughly into two categories: global features and local features. The former category includes texture histogram, color histogram, color layout of the whole image, and features selected from multidimensional discriminant analysis of a collection of images [Faloutsos et al., 1994; Gupta and Jain, 1997; Pentland et al., 1996; Smith and Chang, 1996; Swets and Weng, 1996]. In the latter category are color, texture, and shape features for subimages [Picard and Minka, 1995], segmented regions [Carson et al., 2002; Chen and Wang, 2002; Ma and Manjunath, 1997; Wang et al., 2001b], and interest points [Schmid and Mohr, 1997].
As a relatively mature method, histogram matching has been applied to many general-purpose image retrieval systems such as IBM QBIC [Faloutsos et al., 1994], MIT Photobook [Pentland et al., 1996], Virage System [Gupta and Jain, 1997], and Columbia VisualSEEK and WebSEEK [Smith and Chang, 1996], etc. The Mahalanobis distance [Hafner et al., 1995] and intersection distance [Swain and Ballard, 1991] are commonly used to compute the difference between two histograms with the same number of bins. When the number of bins are different, e.g., when a sparse representation is used, the Earth Mover’s Distance (EMD) [Rubner et al., 1997] applies. The EMD is computed by solving a linear programming problem. A major drawback of the global histogram search lies in its sensitivity to intensity variations, color distortions, and cropping.
Many approaches have been proposed to tackle this problem:
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The PicToSeek [Gevers and Smeulders, 2000] system uses color models invariant to object geometry, object pose, and illumination.
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VisualSEEK and Virage systems attempt to reduce the influence of intensity variations and color distortions by employing spatial rela tionships and color layout in addition to those elementary color, texture, and shape features.
* The same idea of color layout indexing is extended in a later system, Stanford WBIIS [Wang et al., 1998], which, instead of averaging, characterizes the color variations over the spatial extent of an image by Daubechies’ wavelet coefficients and their variances.
* Schmid and Mohr [Schmid and Mohr, 1997] proposed a method of indexing images based on local features of automatically detected interest points of images.
* Minka and Picard [Minka and Picard, 1997] described a learning algorithm for selecting and grouping features. The user guides the learning process by providing positive and negative examples.
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The approach presented in [Swets and Weng, 1996] uses what is called the Most Discriminating Features for image retrieval. These features are extracted from a set of training images by optimal linear projection.
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The Virage system allows users to adjust weights of implemented features according to their own perceptions. The PicHunter system [Cox et al., 2000] and the UIUC MARS [Mehrotra et al., 1997] system are self-adaptable to different applications and different users based upon user feedbacks.
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To approximate the human perception of the shapes of the objects in the images, Del Bimbo and Pala [Bimbo and Pala, 1997] introduced a measure of shape similarity using elastic matching.
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In [Mojsilovic et al., 2000], matching and retrieval are performed along what is referred to as perceptual dimensions which are obtained from subjective experiments and multidimensional scaling based on the model of human perception of color patterns.
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In [Berretti et al., 2000], two distinct similarity measures, concerning respectively with fitting human perception and with the efficiency of data organization and indexing, are proposed for content-based image retrieval by shape similarity.
Contents
7
Preface
13
Acknowledgments
16
Chapter 1 INTRODUCTION
17
1. Text-Based Image Retrieval
17
2. Content-Based Image Retrieval
19
3. Automatic Linguistic Indexing of Images
20
4. Applications of Image Indexing and Retrieval
21
4.1 Web- Related Applications
21
4.2 Biomedical Applications
23
4.3 Space Science
24
4.4 Other Applications
26
5. Contributions of the Book
26
5.1 A Robust Image Similarity Measure
26
5.2 Clustering- Based Retrieval
27
5.3 Learning and Reasoning with Regions
28
5.4 Automatic Linguistic Indexing
28
5.5 Modeling Ancient Paintings
29
6. The Structure of the Book
30
Chapter 2 IMAGE RETRIEVAL AND LINGUISTIC INDEXING
31
1. Introduction
31
2. Content-Based Image Retrieval
31
2.1 Similarity Comparison
32
2.2 Semantic Gap
34
3. Categorization and Linguistic Indexing
36
4. Summary
39
Chapter 3 MACHINE LEARNING AND STATISTICAL MODELING
41
1. Introduction
41
2. Spectral Graph Clustering
41
3. VC Theory and Support Vector Machines
44
3.1 VC Theory
45
3.2 Support Vector Machines
46
4. Additive Fuzzy Systems
50
5. Support Vector Learning for Fuzzy Rule-Based Classification Systems
52
5.1 Additive Fuzzy Rule- Based Classification Systems
53
5.2 Positive Definite Fuzzy Classifiers
54
5.3 An SVM Approach to Build Positive Definite Fuzzy Classifiers
56
6. 2-D Multi-Resolution Hidden Markov Models
58
7. Summary
62
Chapter 4 A ROBUST REGION-BASEDSIMILARITY MEASURE
63
1. Introduction
63
2. Image Segmentation and Representation
65
2.1 Image Segmentation
65
2.2 Fuzzy Feature Representation of an Image
67
2.3 An Algorithmic View
71
3. Unified Feature Matching
72
3.1 Similarity Between Regions
72
3.2 Fuzzy Feature Matching
74
3.3 The UFM Measure
76
3.4 An Algorithmic View
78
4. An Algorithmic Summarization of the System
79
5. Experiments
80
5.1 Query Examples
80
5.2 Systematic Evaluation
80
5.3 Speed
87
5.4 Comparison of Membership Functions
88
6. Summary
89
Chapter 5 CLUSTER-BASED RETRIEVALBY UNSUPERVISED LEARNING
91
1. Introduction
91
2. Retrieval of Similarity Induced Image Clusters
92
2.1 System Overview
92
2.2 Neighboring Target Images Selection
93
2.3 Spectral Graph Partitioning
94
2.4 Finding a Representative Image for a Cluster
95
3. An Algorithmic View
96
3.1 Outline of Algorithm
96
3.2 Organization of Clusters
98
3.3 Computational Complexity
99
3.4 Parameters Selection
100
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