by Polly Y. Yuen, Aegidia Y. Wong. / Bibliography: leaves 92-95 / Thesis (M.B.A.)--Chinese University of Hong Kong, 1986

Peng, Xiang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 98-115). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Statement --- p.3 / Chapter 1.2 --- Major Contributions --- p.6 / Chapter 1.3 --- Thesis Outline --- p.7 / Chapter 2 --- Background Study --- p.9 / Chapter 2.1 --- Content-based Image Retrieval --- p.9 / Chapter 2.1.1 --- Image Representation --- p.11 / Chapter 2.1.2 --- High Dimensional Indexing --- p.15 / Chapter 2.1.3 --- Image Retrieval Systems Design --- p.16 / Chapter 2.2 --- Relevance Feedback --- p.19 / Chapter 2.2.1 --- Self-Organizing Map in Relevance Feedback --- p.20 / Chapter 2.2.2 --- Decision Tree in Relevance Feedback --- p.22 / Chapter 2.2.3 --- Bayesian Classifier in Relevance Feedback --- p.24 / Chapter 2.2.4 --- Nearest Neighbor Search in Relevance Feedback --- p.25 / Chapter 2.2.5 --- Support Vector Machines in Relevance Feedback --- p.26 / Chapter 2.3 --- Imbalanced Classification --- p.29 / Chapter 2.4 --- Active Learning --- p.31 / Chapter 2.4.1 --- Uncertainly-based Sampling --- p.33 / Chapter 2.4.2 --- Error Reduction --- p.34 / Chapter 2.4.3 --- Batch Selection --- p.35 / Chapter 2.5 --- Convex Optimization --- p.35 / Chapter 2.5.1 --- Overview of Convex Optimization --- p.35 / Chapter 2.5.2 --- Linear Program --- p.37 / Chapter 2.5.3 --- Quadratic Program --- p.37 / Chapter 2.5.4 --- Quadratically Constrained Quadratic Program --- p.37 / Chapter 2.5.5 --- Cone Program --- p.38 / Chapter 2.5.6 --- Semi-definite Program --- p.39 / Chapter 3 --- Imbalanced Learning with BMPM for CBIR --- p.40 / Chapter 3.1 --- Research Motivation --- p.41 / Chapter 3.2 --- Background Review --- p.42 / Chapter 3.2.1 --- Relevance Feedback for CBIR --- p.42 / Chapter 3.2.2 --- Minimax Probability Machine --- p.42 / Chapter 3.2.3 --- Extensions of Minimax Probability Machine --- p.44 / Chapter 3.3 --- Relevance Feedback using BMPM --- p.45 / Chapter 3.3.1 --- Model Definition --- p.45 / Chapter 3.3.2 --- Advantages of BMPM in Relevance Feedback --- p.46 / Chapter 3.3.3 --- Relevance Feedback Framework by BMPM --- p.47 / Chapter 3.4 --- Experimental Results --- p.47 / Chapter 3.4.1 --- Experiment Datasets --- p.48 / Chapter 3.4.2 --- Performance Evaluation --- p.50 / Chapter 3.4.3 --- Discussions --- p.53 / Chapter 3.5 --- Summary --- p.53 / Chapter 4 --- BMPM Active Learning for CBIR --- p.55 / Chapter 4.1 --- Problem Statement and Motivation --- p.55 / Chapter 4.2 --- Background Review --- p.57 / Chapter 4.3 --- Relevance Feedback by BMPM Active Learning . --- p.58 / Chapter 4.3.1 --- Active Learning Concept --- p.58 / Chapter 4.3.2 --- General Approaches for Active Learning . --- p.59 / Chapter 4.3.3 --- Biased Minimax Probability Machine --- p.60 / Chapter 4.3.4 --- Proposed Framework --- p.61 / Chapter 4.4 --- Experimental Results --- p.63 / Chapter 4.4.1 --- Experiment Setup --- p.64 / Chapter 4.4.2 --- Performance Evaluation --- p.66 / Chapter 4.5 --- Summary --- p.68 / Chapter 5 --- Large Scale Learning with BMPM --- p.70 / Chapter 5.1 --- Introduction --- p.71 / Chapter 5.1.1 --- Motivation --- p.71 / Chapter 5.1.2 --- Contribution --- p.72 / Chapter 5.2 --- Background Review --- p.72 / Chapter 5.2.1 --- Second Order Cone Program --- p.72 / Chapter 5.2.2 --- General Methods for Large Scale Problems --- p.73 / Chapter 5.2.3 --- Biased Minimax Probability Machine --- p.75 / Chapter 5.3 --- Efficient BMPM Training --- p.78 / Chapter 5.3.1 --- Proposed Strategy --- p.78 / Chapter 5.3.2 --- Kernelized BMPM and Its Solution --- p.81 / Chapter 5.4 --- Experimental Results --- p.82 / Chapter 5.4.1 --- Experimental Testbeds --- p.83 / Chapter 5.4.2 --- Experimental Settings --- p.85 / Chapter 5.4.3 --- Performance Evaluation --- p.87 / Chapter 5.5 --- Summary --- p.92 / Chapter 6 --- Conclusion and Future Work --- p.93 / Chapter 6.1 --- Conclusion --- p.93 / Chapter 6.2 --- Future Work --- p.94 / Chapter A --- List of Symbols and Notations --- p.96 / Chapter B --- List of Publications --- p.98 / Bibliography --- p.100

Ma, Ka Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 82-87). / Abstract and appendix in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Objective of This Research --- p.4 / Chapter 1.2 --- Organization of This Thesis --- p.5 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Textual - Image Query Approach --- p.7 / Chapter 2.1.1 --- Yahoo! Image Surfer --- p.7 / Chapter 2.1.2 --- QBIC (Query By Image Content) --- p.8 / Chapter 2.2 --- Feature-based Approach --- p.9 / Chapter 2.2.1 --- Texture Thesaurus for Aerial Photos --- p.9 / Chapter 2.3 --- Caption-aided Approach --- p.10 / Chapter 2.3.1 --- PICTION (Picture and capTION) --- p.10 / Chapter 2.3.2 --- MARIE --- p.11 / Chapter 2.4 --- Summary --- p.11 / Chapter 3 --- Caption Generation --- p.13 / Chapter 3.1 --- System Architecture --- p.13 / Chapter 3.2 --- Domain Pool --- p.15 / Chapter 3.3 --- Image Feature Extraction --- p.16 / Chapter 3.3.1 --- Preprocessing --- p.16 / Chapter 3.3.2 --- Image Segmentation --- p.17 / Chapter 3.4 --- Classification --- p.24 / Chapter 3.4.1 --- Self-Organizing Map (SOM) --- p.26 / Chapter 3.4.2 --- Learning Vector Quantization (LVQ) --- p.28 / Chapter 3.4.3 --- Output of the Classification --- p.30 / Chapter 3.5 --- Caption Generation --- p.30 / Chapter 3.5.1 --- Phase One: Logical Form Generation --- p.31 / Chapter 3.5.2 --- Phase Two: Simplification --- p.32 / Chapter 3.5.3 --- Phase Three: Captioning --- p.33 / Chapter 3.6 --- Summary --- p.35 / Chapter 4 --- Query Examples --- p.37 / Chapter 4.1 --- Query Types --- p.37 / Chapter 4.1.1 --- Non-content-based Retrieval --- p.38 / Chapter 4.1.2 --- Content-based Retrieval --- p.38 / Chapter 4.2 --- Hierarchy Graph --- p.41 / Chapter 4.3 --- Matching --- p.42 / Chapter 4.4 --- Summary --- p.48 / Chapter 5 --- Evaluation --- p.49 / Chapter 5.1 --- Experimental Set-up --- p.50 / Chapter 5.2 --- Experimental Results --- p.51 / Chapter 5.2.1 --- Segmentation --- p.51 / Chapter 5.2.2 --- Classification --- p.53 / Chapter 5.2.3 --- Captioning --- p.55 / Chapter 5.2.4 --- Overall Performance --- p.56 / Chapter 5.3 --- Observations --- p.57 / Chapter 5.4 --- Summary --- p.58 / Chapter 6 --- Another Application --- p.59 / Chapter 6.1 --- Police Force Crimes Investigation --- p.59 / Chapter 6.1.1 --- Image Feature Extraction --- p.61 / Chapter 6.1.2 --- Caption Generation --- p.64 / Chapter 6.1.3 --- Query --- p.66 / Chapter 6.2 --- An Illustrative Example --- p.68 / Chapter 6.3 --- Summary --- p.72 / Chapter 7 --- Conclusions --- p.74 / Chapter 7.1 --- Contribution --- p.77 / Chapter 7.2 --- Future Work --- p.78 / Bibliography --- p.81 / Appendices --- p.88 / Chapter A --- Segmentation Result Under Different Parametes --- p.89 / Chapter B --- Segmentation Time of 10 Randomly Selected Images --- p.90 / Chapter C --- Sample Captions --- p.93

Yue Che Wang, Kenneth. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 100-106). / Abstract also in Chinese. / Abstract --- p.i / Abstract (Chinese) --- p.iv / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The World-Wide Web --- p.1 / Chapter 1.2 --- Searching Information on the WWW --- p.2 / Chapter 1.3 --- Intelligent content-based information discovery on the Web --- p.4 / Chapter 1.4 --- Organization of the Thesis --- p.7 / Chapter 2 --- Literature Review --- p.9 / Chapter 2.1 --- Search Engines --- p.9 / Chapter 2.2 --- Information Indexing Systems --- p.11 / Chapter 2.3 --- Agent-based Systems --- p.13 / Chapter 2.4 --- Information Filtering Systems --- p.16 / Chapter 3 --- Overview of the Proposed Approach --- p.20 / Chapter 3.1 --- System Architecture --- p.21 / Chapter 3.2 --- Topic Profile Specification --- p.25 / Chapter 3.3 --- Text Representation --- p.29 / Chapter 3.3.1 --- Profile Feature Representation --- p.30 / Chapter 3.3.2 --- Document Feature Representation --- p.33 / Chapter 3.4 --- Advantages of the Topic Profile Specifications --- p.34 / Chapter 4 --- Relevance Score Evaluation Process and Relevance Feedback Model --- p.36 / Chapter 4.1 --- Term Weights --- p.37 / Chapter 4.2 --- Document Evaluation through Relevance Score --- p.39 / Chapter 4.3 --- Learning via Relevance Feedback --- p.42 / Chapter 4.3.1 --- Introduction to Relevance Feedback --- p.43 / Chapter 4.3.2 --- Feature Extraction from the Relevance Feedback Models --- p.44 / Chapter 4.3.3 --- Topic Feature Vectors Refinement --- p.49 / Chapter 5 --- Intelligent Web Exploration --- p.51 / Chapter 5.1 --- Introduction to Simulated Annealing --- p.51 / Chapter 5.2 --- Intelligent Web Exploration by Simulated Annealing --- p.54 / Chapter 5.2.1 --- Mathematical Setting of the Discovery Process --- p.57 / Chapter 5.2.2 --- The Entire Exploration Algorithm --- p.58 / Chapter 5.3 --- Incorporating with the Relevance Feedback Model --- p.60 / Chapter 6 --- Experimental Results --- p.61 / Chapter 6.1 --- The Design of the Experiments --- p.61 / Chapter 6.2 --- Experiments on the Effects of the Simulated Annealing Schedule upon the Discovery Precision --- p.65 / Chapter 6.2.1 --- Experiment Setup --- p.66 / Chapter 6.2.2 --- Results --- p.66 / Chapter 6.3 --- Experiments on the Index Page Topic Profile Specification --- p.72 / Chapter 6.3.1 --- Experiment Setup --- p.72 / Chapter 6.3.2 --- Results --- p.73 / Chapter 6.4 --- Experiments on the Relevance Feedback with Full-Text Feature Extraction Strategy --- p.75 / Chapter 6.4.1 --- Experiment Setup --- p.75 / Chapter 6.4.2 --- Results --- p.76 / Chapter 6.5 --- Comparisons of the Relevance Feedback Feature Extraction Strate- gies --- p.78 / Chapter 6.5.1 --- Experiment Setup --- p.78 / Chapter 6.5.2 --- Results --- p.79 / Chapter 6.6 --- Comparisons between the Example Page and the Keyword Topic Profile Specifications --- p.82 / Chapter 6.6.1 --- Experiment Setup --- p.83 / Chapter 6.6.2 --- Results --- p.83 / Chapter 6.7 --- Summary from the Experimental Results --- p.87 / Chapter 7 --- Conclusion --- p.91 / Chapter 7.1 --- The Aim of Our Proposed System --- p.91 / Chapter 7.2 --- The Favorable Features and the Effectiveness of Our Proposed System --- p.92 / Chapter 7.3 --- Future Work --- p.94 / Appendix --- p.96 / Chapter A --- List of URLs for the Example Pages --- p.96 / Chapter B --- List of URLs for the Arbitrarily Chosen Index Pages --- p.98 / Bibliography --- p.100

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Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries