A parameter adjustment method for relevance feedback

Various relevance feedback techniques have been applied in Content-Based Image Retrieval (CBIR). By using relevance feedback, CBIR allows the user to progressively refine the system\u27s response to a query. In this paper, after analyzing the feature distributions of positive and negative feedbacks, a new parameter adjustment method for iteratively improving the query vector and adjusting the weights is proposed. Experimental results demonstrate the effectiveness of this method.<br /

Learning effective color features for content based image retrieval in dermatology

We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn favorable feature representations for this special application: limited rank matrix learning vector quantization (LiRaM LVQ) and a Large Margin Nearest Neighbor (LMNN) approach. Both methods use labeled training data and provide a discriminant linear transformation of the original features, potentially to a lower dimensional space. The extracted color features are used to retrieve images from a database by a k-nearest neighbor search. We perform a comparison of retrieval rates achieved with extracted and original features for eight different standard color spaces. We achieved significant improvements in every examined color space. The increase of the mean correct retrieval rate lies between 10% and 27% in the range of k=1–25 retrieved images, and the correct retrieval rate lies between 84% and 64%. We present explicit combinations of RGB and CIE-Lab color features corresponding to healthy and lesion skin. LiRaM LVQ and the computationally more expensive LMNN give comparable results for large values of the method parameter κ of LMNN (κ≥25) while LiRaM LVQ outperforms LMNN for smaller values of κ. We conclude that feature extraction by LiRaM LVQ leads to considerable improvement in color-based retrieval of dermatologic images

Similarity searching in sequence databases under time warping.

Wong, Siu Fung.Thesis submitted in: December 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 77-84).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.viChapter 1 --- Introduction --- p.1Chapter 2 --- Preliminary --- p.6Chapter 2.1 --- Dynamic Time Warping (DTW) --- p.6Chapter 2.2 --- Spatial Indexing --- p.10Chapter 2.3 --- Relevance Feedback --- p.11Chapter 3 --- Literature Review --- p.13Chapter 3.1 --- Searching Sequences under Euclidean Metric --- p.13Chapter 3.2 --- Searching Sequences under Dynamic Time Warping Metric --- p.17Chapter 4 --- Subsequence Matching under Time Warping --- p.21Chapter 4.1 --- Subsequence Matching --- p.22Chapter 4.1.1 --- Sequential Search --- p.22Chapter 4.1.2 --- Indexing Scheme --- p.23Chapter 4.2 --- Lower Bound Technique --- p.25Chapter 4.2.1 --- Properties of Lower Bound Technique --- p.26Chapter 4.2.2 --- Existing Lower Bound Functions --- p.27Chapter 4.3 --- Point-Based indexing --- p.28Chapter 4.3.1 --- Lower Bound for subsequences matching --- p.28Chapter 4.3.2 --- Algorithm --- p.35Chapter 4.4 --- Rectangle-Based indexing --- p.37Chapter 4.4.1 --- Lower Bound for subsequences matching --- p.37Chapter 4.4.2 --- Algorithm --- p.41Chapter 4.5 --- Experimental Results --- p.43Chapter 4.5.1 --- Candidate ratio vs Width of warping window --- p.44Chapter 4.5.2 --- CPU time vs Number of subsequences --- p.45Chapter 4.5.3 --- CPU time vs Width of warping window --- p.46Chapter 4.5.4 --- CPU time vs Threshold --- p.46Chapter 4.6 --- Summary --- p.47Chapter 5 --- Relevance Feedback under Time Warping --- p.49Chapter 5.1 --- Integrating Relevance Feedback with DTW --- p.49Chapter 5.2 --- Query Reformulation --- p.53Chapter 5.2.1 --- Constraint Updating --- p.53Chapter 5.2.2 --- Weight Updating --- p.55Chapter 5.2.3 --- Overall Strategy --- p.58Chapter 5.3 --- Experiments and Evaluation --- p.59Chapter 5.3.1 --- Effectiveness of the strategy --- p.61Chapter 5.3.2 --- Efficiency of the strategy --- p.63Chapter 5.3.3 --- Usability --- p.64Chapter 5.4 --- Summary --- p.71Chapter 6 --- Conclusion --- p.72Chapter A --- Deduction of Data Bounding Hyper-rectangle --- p.74Chapter B --- Proof of Theorem2 --- p.76Bibliography --- p.77Publications --- p.8

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