The Optimisation of Elementary and Integrative Content-Based Image Retrieval Techniques

Image retrieval plays a major role in many image processing applications. However, a number of factors (e.g. rotation, non-uniform illumination, noise and lack of spatial information) can disrupt the outputs of image retrieval systems such that they cannot produce the desired results. In recent years, many researchers have introduced different approaches to overcome this problem. Colour-based CBIR (content-based image retrieval) and shape-based CBIR were the most commonly used techniques for obtaining image signatures. Although the colour histogram and shape descriptor have produced satisfactory results for certain applications, they still suffer many theoretical and practical problems. A prominent one among them is the well-known “curse of dimensionality “. In this research, a new Fuzzy Fusion-based Colour and Shape Signature (FFCSS) approach for integrating colour-only and shape-only features has been investigated to produce an effective image feature vector for database retrieval. The proposed technique is based on an optimised fuzzy colour scheme and robust shape descriptors. Experimental tests were carried out to check the behaviour of the FFCSS-based system, including sensitivity and robustness of the proposed signature of the sampled images, especially under varied conditions of, rotation, scaling, noise and light intensity. To further improve retrieval efficiency of the devised signature model, the target image repositories were clustered into several groups using the k-means clustering algorithm at system runtime, where the search begins at the centres of each cluster. The FFCSS-based approach has proven superior to other benchmarked classic CBIR methods, hence this research makes a substantial contribution towards corresponding theoretical and practical fronts

Content-based image retrieval-- a small sample learning approach.

Tao Dacheng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 70-75).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Content-based Image Retrieval --- p.1Chapter 1.2 --- SVM based RF in CBIR --- p.3Chapter 1.3 --- DA based RF in CBIR --- p.4Chapter 1.4 --- Existing CBIR Engines --- p.5Chapter 1.5 --- Practical Applications of CBIR --- p.10Chapter 1.6 --- Organization of this thesis --- p.11Chapter Chapter 2 --- Statistical Learning Theory and Support Vector Machine --- p.12Chapter 2.1 --- The Recognition Problem --- p.12Chapter 2.2 --- Regularization --- p.14Chapter 2.3 --- The VC Dimension --- p.14Chapter 2.4 --- Structure Risk Minimization --- p.15Chapter 2.5 --- Support Vector Machine --- p.15Chapter 2.6 --- Kernel Space --- p.17Chapter Chapter 3 --- Discriminant Analysis --- p.18Chapter 3.1 --- PCA --- p.18Chapter 3.2 --- KPCA --- p.18Chapter 3.3 --- LDA --- p.20Chapter 3.4 --- BDA --- p.20Chapter 3.5 --- KBDA --- p.21Chapter Chapter 4 --- Random Sampling Based SVM --- p.24Chapter 4.1 --- Asymmetric Bagging SVM --- p.25Chapter 4.2 --- Random Subspace Method SVM --- p.26Chapter 4.3 --- Asymmetric Bagging RSM SVM --- p.26Chapter 4.4 --- Aggregation Model --- p.30Chapter 4.5 --- Dissimilarity Measure --- p.31Chapter 4.6 --- Computational Complexity Analysis --- p.31Chapter 4.7 --- QueryGo Image Retrieval System --- p.32Chapter 4.8 --- Toy Experiments --- p.35Chapter 4.9 --- Statistical Experimental Results --- p.36Chapter Chapter 5 --- SSS Problems in KBDA RF --- p.42Chapter 5.1 --- DKBDA --- p.43Chapter 5.1.1 --- DLDA --- p.43Chapter 5.1.2 --- DKBDA --- p.43Chapter 5.2 --- NKBDA --- p.48Chapter 5.2.1 --- NLDA --- p.48Chapter 5.2.2 --- NKBDA --- p.48Chapter 5.3 --- FKBDA --- p.49Chapter 5.3.1 --- FLDA --- p.49Chapter 5.3.2 --- FKBDA --- p.49Chapter 5.4 --- Experimental Results --- p.50Chapter Chapter 6 --- NDA based RF for CBIR --- p.52Chapter 6.1 --- NDA --- p.52Chapter 6.2 --- SSS Problem in NDA --- p.53Chapter 6.2.1 --- Regularization method --- p.53Chapter 6.2.2 --- Null-space method --- p.54Chapter 6.2.3 --- Full-space method --- p.54Chapter 6.3 --- Experimental results --- p.55Chapter 6.3.1 --- K nearest neighbor evaluation for NDA --- p.55Chapter 6.3.2 --- SSS problem --- p.56Chapter 6.3.3 --- Evaluation experiments --- p.57Chapter Chapter 7 --- Medical Image Classification --- p.59Chapter 7.1 --- Introduction --- p.59Chapter 7.2 --- Region-based Co-occurrence Matrix Texture Feature --- p.60Chapter 7.3 --- Multi-level Feature Selection --- p.62Chapter 7.4 --- Experimental Results --- p.63Chapter 7.4.1 --- Data Set --- p.64Chapter 7.4.2 --- Classification Using Traditional Features --- p.65Chapter 7.4.3 --- Classification Using the New Features --- p.66Chapter Chapter 8 --- Conclusion --- p.68Bibliography --- p.7

Interactive video retrieval using implicit user feedback.

PhDIn the recent years, the rapid development of digital technologies and the low cost of recording media have led to a great increase in the availability of multimedia content worldwide. This availability places the demand for the development of advanced search engines. Traditionally, manual annotation of video was one of the usual practices to support retrieval. However, the vast amounts of multimedia content make such practices very expensive in terms of human effort. At the same time, the availability of low cost wearable sensors delivers a plethora of user-machine interaction data. Therefore, there is an important challenge of exploiting implicit user feedback (such as user navigation patterns and eye movements) during interactive multimedia retrieval sessions with a view to improving video search engines. In this thesis, we focus on automatically annotating video content by exploiting aggregated implicit feedback of past users expressed as click-through data and gaze movements. Towards this goal, we have conducted interactive video retrieval experiments, in order to collect click-through and eye movement data in not strictly controlled environments. First, we generate semantic relations between the multimedia items by proposing a graph representation of aggregated past interaction data and exploit them to generate recommendations, as well as to improve content-based search. Then, we investigate the role of user gaze movements in interactive video retrieval and propose a methodology for inferring user interest by employing support vector machines and gaze movement-based features. Finally, we propose an automatic video annotation framework, which combines query clustering into topics by constructing gaze movement-driven random forests and temporally enhanced dominant sets, as well as video shot classification for predicting the relevance of viewed items with respect to a topic. The results show that exploiting heterogeneous implicit feedback from past users is of added value for future users of interactive video retrieval systems

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Sparse machine learning methods with applications in multivariate signal processing

This thesis details theoretical and empirical work that draws from two main subject areas: Machine Learning (ML) and Digital Signal Processing (DSP). A unified general framework is given for the application of sparse machine learning methods to multivariate signal processing. In particular, methods that enforce sparsity will be employed for reasons of computational efficiency, regularisation, and compressibility. The methods presented can be seen as modular building blocks that can be applied to a variety of applications. Application specific prior knowledge can be used in various ways, resulting in a flexible and powerful set of tools. The motivation for the methods is to be able to learn and generalise from a set of multivariate signals. In addition to testing on benchmark datasets, a series of empirical evaluations on real world datasets were carried out. These included: the classification of musical genre from polyphonic audio files; a study of how the sampling rate in a digital radar can be reduced through the use of Compressed Sensing (CS); analysis of human perception of different modulations of musical key from Electroencephalography (EEG) recordings; classification of genre of musical pieces to which a listener is attending from Magnetoencephalography (MEG) brain recordings. These applications demonstrate the efficacy of the framework and highlight interesting directions of future research