Hoi, Chu-Hong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 89-103). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Content-based Image Retrieval --- p.1 / Chapter 1.2 --- Relevance Feedback --- p.3 / Chapter 1.3 --- Contributions --- p.4 / Chapter 1.4 --- Organization of This Work --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Relevance Feedback --- p.8 / Chapter 2.1.1 --- Heuristic Weighting Methods --- p.9 / Chapter 2.1.2 --- Optimization Formulations --- p.10 / Chapter 2.1.3 --- Various Machine Learning Techniques --- p.11 / Chapter 2.2 --- Support Vector Machines --- p.12 / Chapter 2.2.1 --- Setting of the Learning Problem --- p.12 / Chapter 2.2.2 --- Optimal Separating Hyperplane --- p.13 / Chapter 2.2.3 --- Soft-Margin Support Vector Machine --- p.15 / Chapter 2.2.4 --- One-Class Support Vector Machine --- p.16 / Chapter 3 --- Relevance Feedback with Biased SVM --- p.18 / Chapter 3.1 --- Introduction --- p.18 / Chapter 3.2 --- Biased Support Vector Machine --- p.19 / Chapter 3.3 --- Relevance Feedback Using Biased SVM --- p.22 / Chapter 3.3.1 --- Advantages of BSVM in Relevance Feedback --- p.22 / Chapter 3.3.2 --- Relevance Feedback Algorithm by BSVM --- p.23 / Chapter 3.4 --- Experiments --- p.24 / Chapter 3.4.1 --- Datasets --- p.24 / Chapter 3.4.2 --- Image Representation --- p.25 / Chapter 3.4.3 --- Experimental Results --- p.26 / Chapter 3.5 --- Discussions --- p.29 / Chapter 3.6 --- Summary --- p.30 / Chapter 4 --- Optimizing Learning with SVM Constraint --- p.31 / Chapter 4.1 --- Introduction --- p.31 / Chapter 4.2 --- Related Work and Motivation --- p.33 / Chapter 4.3 --- Optimizing Learning with SVM Constraint --- p.35 / Chapter 4.3.1 --- Problem Formulation and Notations --- p.35 / Chapter 4.3.2 --- Learning boundaries with SVM --- p.35 / Chapter 4.3.3 --- OPL for the Optimal Distance Function --- p.38 / Chapter 4.3.4 --- Overall Similarity Measure with OPL and SVM --- p.40 / Chapter 4.4 --- Experiments --- p.41 / Chapter 4.4.1 --- Datasets --- p.41 / Chapter 4.4.2 --- Image Representation --- p.42 / Chapter 4.4.3 --- Performance Evaluation --- p.43 / Chapter 4.4.4 --- Complexity and Time Cost Evaluation --- p.45 / Chapter 4.5 --- Discussions --- p.47 / Chapter 4.6 --- Summary --- p.48 / Chapter 5 --- Group-based Relevance Feedback --- p.49 / Chapter 5.1 --- Introduction --- p.49 / Chapter 5.2 --- SVM Ensembles --- p.50 / Chapter 5.3 --- Group-based Relevance Feedback Using SVM Ensembles --- p.51 / Chapter 5.3.1 --- (x+l)-class Assumption --- p.51 / Chapter 5.3.2 --- Proposed Architecture --- p.52 / Chapter 5.3.3 --- Strategy for SVM Combination and Group Ag- gregation --- p.52 / Chapter 5.4 --- Experiments --- p.54 / Chapter 5.4.1 --- Experimental Implementation --- p.54 / Chapter 5.4.2 --- Performance Evaluation --- p.55 / Chapter 5.5 --- Discussions --- p.56 / Chapter 5.6 --- Summary --- p.57 / Chapter 6 --- Log-based Relevance Feedback --- p.58 / Chapter 6.1 --- Introduction --- p.58 / Chapter 6.2 --- Related Work and Motivation --- p.60 / Chapter 6.3 --- Log-based Relevance Feedback Using SLSVM --- p.61 / Chapter 6.3.1 --- Problem Statement --- p.61 / Chapter 6.3.2 --- Soft Label Support Vector Machine --- p.62 / Chapter 6.3.3 --- LRF Algorithm by SLSVM --- p.64 / Chapter 6.4 --- Experimental Results --- p.66 / Chapter 6.4.1 --- Datasets --- p.66 / Chapter 6.4.2 --- Image Representation --- p.66 / Chapter 6.4.3 --- Experimental Setup --- p.67 / Chapter 6.4.4 --- Performance Comparison --- p.68 / Chapter 6.5 --- Discussions --- p.73 / Chapter 6.6 --- Summary --- p.75 / Chapter 7 --- Application: Web Image Learning --- p.76 / Chapter 7.1 --- Introduction --- p.76 / Chapter 7.2 --- A Learning Scheme for Searching Semantic Concepts --- p.77 / Chapter 7.2.1 --- Searching and Clustering Web Images --- p.78 / Chapter 7.2.2 --- Learning Semantic Concepts with Relevance Feed- back --- p.73 / Chapter 7.3 --- Experimental Results --- p.79 / Chapter 7.3.1 --- Dataset and Features --- p.79 / Chapter 7.3.2 --- Performance Evaluation --- p.80 / Chapter 7.4 --- Discussions --- p.82 / Chapter 7.5 --- Summary --- p.82 / Chapter 8 --- Conclusions and Future Work --- p.84 / Chapter 8.1 --- Conclusions --- p.84 / Chapter 8.2 --- Future Work --- p.85 / Chapter A --- List of Publications --- p.87 / Bibliography --- p.103
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_324884 |
| Date | January 2004 |
| Contributors | Hoi, Chu-Hong., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xii, 103 leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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