| Description |
1 online resource (154 pages) |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
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text file |
| Series |
Computer science reviews and trends |
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Computer science reviews and trends.
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| Bibliography |
Includes bibliographical references. |
| Summary |
Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine, mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging research topic. View-based 3-D Object Retrieval introduces and discusses the fundamental challenges in view-based 3-D object retrieval, proposes a collection of selected state-of-the-art methods for accomplishing this task developed by the authors, and summarizes recent achievements in view-based 3-D object retrieval. Part I presents an Introduction to View-based 3-D Object Retrieval, Part II discusses View Extraction, Selection, and Representation, Part III provides a deep dive into View-Based 3-D Object Comparison, and Part IV looks at future research and developments including Big Data application and geographical location-based applications. Systematically introduces view-based 3-D object retrieval, including problem definitions and settings, methodologies, and benchmark testing beds Discusses several key challenges in view-based 3-D object retrieval, and introduces the state-of-the-art solutions Presents the progression from general image retrieval techniques to view-based 3-D object retrieval Introduces future research efforts in the areas of Big Data, feature extraction, and geographical location-based applications. |
| Contents |
Front Cover; View-Based 3-D Object Retrieval; Copyright; Contents; Acknowledgments; Preface; Part I: The Start; Chapter 1: Introduction; 1.1 The Definition of 3DOR; 1.2 Model-Based 3DOR Versus View-Based 3DOR; 1.3 The Challenges of V3DOR; 1.4 Summary of Our Work; 1.4.1 View Extraction; 1.4.2 Representative View Selection; 1.4.3 Learning the Weights for Multiple Views; 1.4.4 Distance Measures for Object Matching; 1.4.5 Learning the Relevance Among 3-D Objects; 1.5 Structure of This Book; 1.6 Summary; References; Chapter 2: The Benchmark and Evaluation; 2.1 Introduction |
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2.2 The Standard Benchmarks2.3 The Shape Retrieval Contest; 2.4 Evaluation Criteria in 3DOR; 2.5 Summary; References; Part II View Extraction, Selection, and Representation; Chapter 3: View Extraction; 3.1 Introduction; 3.2 Dense Sampling Viewpoints; 3.3 Predefined Camera Array; 3.4 Generated View; 3.5 Summary; References; Chapter 4: View Selection; 4.1 Introduction; 4.2 Unsupervised View Selection; 4.3 Interactive View Selection; 4.3.1 Multiview 3-D Object Matching; 4.3.2 View Clustering; 4.3.3 Initial Query View Selection; 4.3.4 Interactive View Selection with User Relevance Feedback |
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4.3.5 Learning a Distance Metric4.3.6 Multiple Query Views Linear Combination; 4.3.7 The Computational Cost; 4.4 Summary; References; Chapter 5: View Representation; 5.1 Introduction; 5.2 Shape Feature Extraction; 5.2.1 Zernike Moments; 5.2.2 Fourier Descriptor; 5.3 The Bag-of-Visual-Features Method; 5.3.1 The Bag-of-Visual-Words; 5.3.2 The Bag-of-Region-Words; 5.4 Learning the Weights for Multiple Views; 5.4.1 K-Partite Graph Reinforcement; 5.4.2 Weight Learning for Multiple Views Usingthe k-Partite Graph; 5.5 Summary; References; Part III View-Based 3-D Object Comparison |
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Chapter 6: Multiple-View Distance Metric6.1 Introduction; 6.2 Fundamental Many-to-Many Distance Measures; 6.3 Bipartite Graph Matching; 6.3.1 View Selection and Weighting; 6.3.2 Bipartite Graph Construction; 6.3.3 Bipartite Graph Matching; 6.4 Statistical Matching; 6.4.1 Adaptive View Clustering; 6.4.2 CCFV; 6.4.2.1 View Clustering and Query Model Training; 6.4.2.2 Positive and Negative Matching Models; 6.4.2.3 Calculation of the Similarity Between Q and O S(Q, O); 6.4.2.4 Analysis of Computational Cost; 6.4.3 Markov Chain; 6.4.4 Gaussian Mixture Model Formulation |
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6.4.4.1 Conventional GMM Training6.4.4.2 Generative Adaptation of GMM; 6.4.4.3 Discriminative Adaptation of GMM; 6.4.4.4 Learning the Weights for Multiple GMMs; 6.5 Summary; References; Chapter 7: Learning-Based 3-D Object Retrieval; 7.1 Introduction; 7.2 Learning Optimal Distance Metrics; 7.2.1 Hausdorff Distance Learning; 7.2.2 Learning Bipartite Graph Optimal Matching; 7.3 3-D Object Relevance Estimation via Hypergraph Learning; 7.3.1 Hypergraph and Its Applications; 7.3.2 Learning on Single Hypergraph; 7.3.3 Learning on Multiple Hypergraphs |
| Subject |
Image processing.
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Pattern recognition systems.
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Traitement d'images.
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Reconnaissance des formes (Informatique)
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image processing.
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TECHNOLOGY & ENGINEERING -- Mechanical.
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Image processing
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Pattern recognition systems
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| Added Author |
Dai, Qionghai, author.
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| Other Form: |
Print version: Gao, Yue. View-based 3-D Object Retrieval. Burlington : Elsevier Science, ©2014 9780128024195 |
| ISBN |
9780128026236 (electronic bk.) |
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0128026235 (electronic bk.) |
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9780128024195 |
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0128024194 |
| Standard No. |
AU@ 000064558850 |
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CHNEW 001012647 |
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DEBBG BV042988375 |
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DEBBG BV043615715 |
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DEBSZ 431869227 |
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DEBSZ 434137758 |
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DEBSZ 453330290 |
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GBVCP 817111794 |
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