Using biased support vector machine in image retrieval with self-organizing map.

Chan Chi Hang.Thesis submitted in: August 2004.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 105-114).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Statement --- p.3Chapter 1.2 --- Major Contributions --- p.5Chapter 1.3 --- Publication List --- p.6Chapter 1.4 --- Thesis Organization --- p.7Chapter 2 --- Background Survey --- p.9Chapter 2.1 --- Relevance Feedback Framework --- p.9Chapter 2.1.1 --- Relevance Feedback Types --- p.11Chapter 2.1.2 --- Data Distribution --- p.12Chapter 2.1.3 --- Training Set Size --- p.14Chapter 2.1.4 --- Inter-Query Learning and Intra-Query Learning --- p.15Chapter 2.2 --- History of Relevance Feedback Techniques --- p.16Chapter 2.3 --- Relevance Feedback Approaches --- p.19Chapter 2.3.1 --- Vector Space Model --- p.19Chapter 2.3.2 --- Ad-hoc Re-weighting --- p.26Chapter 2.3.3 --- Distance Optimization Approach --- p.29Chapter 2.3.4 --- Probabilistic Model --- p.33Chapter 2.3.5 --- Bayesian Approach --- p.39Chapter 2.3.6 --- Density Estimation Approach --- p.42Chapter 2.3.7 --- Support Vector Machine --- p.48Chapter 2.4 --- Presentation Set Selection --- p.52Chapter 2.4.1 --- Most-probable strategy --- p.52Chapter 2.4.2 --- Most-informative strategy --- p.52Chapter 3 --- Biased Support Vector Machine for Content-Based Image Retrieval --- p.57Chapter 3.1 --- Motivation --- p.57Chapter 3.2 --- Background --- p.58Chapter 3.2.1 --- Regular Support Vector Machine --- p.59Chapter 3.2.2 --- One-class Support Vector Machine --- p.61Chapter 3.3 --- Biased Support Vector Machine --- p.63Chapter 3.4 --- Interpretation of parameters in BSVM --- p.67Chapter 3.5 --- Soft Label Biased Support Vector Machine --- p.69Chapter 3.6 --- Interpretation of parameters in Soft Label BSVM --- p.73Chapter 3.7 --- Relevance Feedback Using Biased Support Vector Machine --- p.74Chapter 3.7.1 --- Advantages of BSVM in Relevance Feedback . . --- p.74Chapter 3.7.2 --- Relevance Feedback Algorithm By BSVM --- p.75Chapter 3.8 --- Experiments --- p.78Chapter 3.8.1 --- Synthetic Dataset --- p.80Chapter 3.8.2 --- Real-World Dataset --- p.81Chapter 3.8.3 --- Experimental Results --- p.83Chapter 3.9 --- Conclusion --- p.86Chapter 4 --- Self-Organizing Map-based Inter-Query Learning --- p.88Chapter 4.1 --- Motivation --- p.88Chapter 4.2 --- Algorithm --- p.89Chapter 4.2.1 --- Initialization and Replication of SOM --- p.89Chapter 4.2.2 --- SOM Training for Inter-Query Learning --- p.90Chapter 4.2.3 --- Incorporate with Intra-Query Learning --- p.92Chapter 4.3 --- Experiments --- p.93Chapter 4.3.1 --- Synthetic Dataset --- p.95Chapter 4.3.2 --- Real-World Dataset --- p.95Chapter 4.3.3 --- Experimental Results --- p.97Chapter 4.4 --- Conclusion --- p.98Chapter 5 --- Conclusion --- p.102Bibliography --- p.10