Indexing, learning and content-based retrieval for special purpose image databases

This chapter deals with content-based image retrieval in special purpose image databases. As image data is amassed ever more effortlessly, building efficient systems for searching and browsing of image databases becomes increasingly urgent. We provide an overview of the current state-of-the art by taking a tour along the entir

The Methodology and Practice of the Evaluation of Image Retrieval Systems and Segmentation Methods

Content-Based Image Retrieval is important for two reasons. First, the oft-cited growth of image archives in many fields, and the rapid expansion of the Web, mean that successful image retrieval systems are fast becoming a necessity if the mass of accumulated data is to be useful. Second, database retrieval provides a framework within which the important questions of machine vision are brought into focus: successful retrieval is likely to require genuine image understanding. In view of these points, the evaluatio- n of retrieval systems becomes a matter of priority. There is already a substantial literature evaluating specific systems, but little high-level discussion of the evaluation methodologies themselves seems to have taken place. In the first part of the report, we propose a framework within which such issues can be addressed, analyse possible evaluation methodologies, indicate where they are appropriate and where they are not, and critique query-by-example and evaluation methodologies related to it. In the second part of the report, we apply the results of this analysis to a particular dataset. The dataset is problematic but typical: no ground truth is available for its semantics. Considering retrieval based on image segmentation- s, we present a novel method for its evaluation. Unlike methods of evaluation that rely on the existence or creation of ground truth, the proposed evaluatio- n procedure subjects human subjects to a psychovisual test comparing the results of different segmentation schemes. The test is designed to answer two questions: does consensus about a `best' segmentation exist, and if it does, what do we learn about segmentation schemes for retrieval? The results confirm that human subjects are consistent in their judgements, thus allowing meaningful evaluation

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