797 research outputs found
A survey of kernel and spectral methods for clustering
Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods. The aim of this paper is to present a survey of kernel and spectral clustering methods, two approaches able to produce nonlinear separating hypersurfaces between clusters. The presented kernel clustering methods are the kernel version of many classical clustering algorithms, e.g., K-means, SOM and neural gas. Spectral clustering arise from concepts in spectral graph theory and the clustering problem is configured as a graph cut problem where an appropriate objective function has to be optimized. An explicit proof of the fact that these two paradigms have the same objective is reported since it has been proven that these two seemingly different approaches have the same mathematical foundation. Besides, fuzzy kernel clustering methods are presented as extensions of kernel K-means clustering algorithm. (C) 2007 Pattem Recognition Society. Published by Elsevier Ltd. All rights reserved
Modelling and tracking objects with a topology preserving self-organising neural network
Human gestures form an integral part in our everyday communication. We use
gestures not only to reinforce meaning, but also to describe the shape of objects,
to play games, and to communicate in noisy environments. Vision systems that
exploit gestures are often limited by inaccuracies inherent in handcrafted models.
These models are generated from a collection of training examples which requires
segmentation and alignment. Segmentation in gesture recognition typically involves manual intervention, a time consuming process that is feasible only for a
limited set of gestures. Ideally gesture models should be automatically acquired
via a learning scheme that enables the acquisition of detailed behavioural knowledge only from topological and temporal observation.
The research described in this thesis is motivated by a desire to provide a framework for the unsupervised acquisition and tracking of gesture models. In any
learning framework, the initialisation of the shapes is very crucial. Hence, it would
be beneficial to have a robust model not prone to noise that can automatically correspond the set of shapes. In the first part of this thesis, we develop a framework
for building statistical 2D shape models by extracting, labelling and corresponding
landmark points using only topological relations derived from competitive hebbian learning. The method is based on the assumption that correspondences can
be addressed as an unsupervised classification problem where landmark points
are the cluster centres (nodes) in a high-dimensional vector space. The approach
is novel in that the network can be used in cases where the topological structure of
the input pattern is not known a priori thus no topology of fixed dimensionality is imposed onto the network.
In the second part, we propose an approach to minimise the user intervention
in the adaptation process, which requires to specify a priori the number of nodes
needed to represent an object, by utilising an automatic criterion for maximum
node growth. Furthermore, this model is used to represent motion in image sequences by initialising a suitable segmentation that separates the object of interest
from the background. The segmentation system takes into consideration some illumination tolerance, images as inputs from ordinary cameras and webcams, some
low to medium cluttered background avoiding extremely cluttered backgrounds,
and that the objects are at close range from the camera.
In the final part, we extend the framework for the automatic modelling and
unsupervised tracking of 2D hand gestures in a sequence of k frames. The aim
is to use the tracked frames as training examples in order to build the model and
maintain correspondences. To do that we add an active step to the Growing Neural Gas (GNG) network, which we call Active Growing Neural Gas (A-GNG) that
takes into consideration not only the geometrical position of the nodes, but also the
underlined local feature structure of the image, and the distance vector between
successive images. The quality of our model is measured through the calculation
of the topographic product. The topographic product is our topology preserving
measure which quantifies the neighbourhood preservation.
In our system we have applied specific restrictions in the velocity and the appearance of the gestures to simplify the difficulty of the motion analysis in the gesture representation. The proposed framework has been validated on applications
related to sign language. The work has great potential in Virtual Reality (VR) applications where the learning and the representation of gestures becomes natural
without the need of expensive wear cable sensors
Self-organising maps : statistical analysis, treatment and applications.
This thesis presents some substantial theoretical analyses and optimal treatments
of Kohonen's self-organising map (SOM) algorithm, and explores the practical
application potential of the algorithm for vector quantisation, pattern classification,
and image processing. It consists of two major parts. In the first part, the SOM
algorithm is investigated and analysed from a statistical viewpoint. The proof of its
universal convergence for any dimensionality is obtained using a novel and
extended form of the Central Limit Theorem. Its feature space is shown to be an
approximate multivariate Gaussian process, which will eventually converge and
form a mapping, which minimises the mean-square distortion between the feature
and input spaces. The diminishing effect of the initial states and implicit effects of
the learning rate and neighbourhood function on its convergence and ordering are
analysed and discussed. Distinct and meaningful definitions, and associated
measures, of its ordering are presented in relation to map's fault-tolerance. The
SOM algorithm is further enhanced by incorporating a proposed constraint, or
Bayesian modification, in order to achieve optimal vector quantisation or pattern
classification. The second part of this thesis addresses the task of unsupervised
texture-image segmentation by means of SOM networks and model-based
descriptions. A brief review of texture analysis in terms of definitions, perceptions,
and approaches is given. Markov random field model-based approaches are
discussed in detail. Arising from this a hierarchical self-organised segmentation
structure, which consists of a local MRF parameter estimator, a SOM network, and
a simple voting layer, is proposed and is shown, by theoretical analysis and
practical experiment, to achieve a maximum likelihood or maximum a posteriori
segmentation. A fast, simple, but efficient boundary relaxation algorithm is
proposed as a post-processor to further refine the resulting segmentation. The class
number validation problem in a fully unsupervised segmentation is approached by
a classical, simple, and on-line minimum mean-square-error method. Experimental
results indicate that this method is very efficient for texture segmentation
problems. The thesis concludes with some suggestions for further work on SOM
neural networks
A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph
Flexible and efficient variants of the Self Organizing Map algorithm have been proposed for non vector data, including, for example, the dissimilarity SOM (also called the Median SOM) and several kernelized versions of SOM. Although the first one is a generalization of the batch version of the SOM algorithm to data described by a dissimilarity measure, the various versions of the second ones are stochastic SOM. We propose here to introduce a batch version of the kernel SOM and to show how this one is related to the dissimilarity SOM. Finally, an application to the classification of the vertices of a graph is proposed and the algorithms are tested and compared on a simulated data set
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
Development and Application of Chemometric Methods for Modelling Metabolic Spectral Profiles
The interpretation of metabolic information is crucial to understanding the functioning of a biological
system. Latent information about the metabolic state of a sample can be acquired using
analytical chemistry methods, which generate spectroscopic profiles. Thus, nuclear magnetic resonance
spectroscopy and mass spectrometry techniques can be employed to generate vast amounts
of highly complex data on the metabolic content of biofluids and tissue, and this thesis discusses
ways to process, analyse and interpret these data successfully.
The evaluation of J -resolved spectroscopy in magnetic resonance profiling and the statistical
techniques required to extract maximum information from the projections of these spectra are
studied. In particular, data processing is evaluated, and correlation and regression methods are
investigated with respect to enhanced model interpretation and biomarker identification. Additionally,
it is shown that non-linearities in metabonomic data can be effectively modelled with
kernel-based orthogonal partial least squares, for which an automated optimisation of the kernel
parameter with nested cross-validation is implemented. The interpretation of orthogonal variation
and predictive ability enabled by this approach are demonstrated in regression and classification
models for applications in toxicology and parasitology. Finally, the vast amount of data generated
with mass spectrometry imaging is investigated in terms of data processing, and the benefits of
applying multivariate techniques to these data are illustrated, especially in terms of interpretation
and visualisation using colour-coding of images. The advantages of methods such as principal
component analysis, self-organising maps and manifold learning over univariate analysis are highlighted.
This body of work therefore demonstrates new means of increasing the amount of biochemical
information that can be obtained from a given set of samples in biological applications using
spectral profiling. Various analytical and statistical methods are investigated and illustrated with
applications drawn from diverse biomedical areas
Computerised electrocardiogram classification
Advances in computing have resulted in many engineering processes being automated. Electrocardiogram (ECG) classification is one such process. The analysis of ECGs can benefit from the wide availability and power of modern computers. This study presents the usage of computer technology in the field of computerised ECG classification. Computerised electrocardiogram classification can help to reduce healthcare costs by enabling suitably equipped general practitioners to refer to hospital only those people with serious heart problems. Computerised ECG classification can also be very useful in shortening hospital waiting lists and saving life by discovering heart diseases early. The thesis investigates the automatic classification of ECGs into different disease categories using Artificial Intelligence (AI) techniques. A comparison of the use of different feature sets and AI classifiers is presented. The feature sets include conventional cardiological features, as well as features taken directly from time domain samples of an ECG. The benchmark AI classifiers tested include those based on neural network, k-Nearest Neighbour and inductive learning techniques. The research proposes two modifications to the learning vector quantisation (LVQ) neural network, namely the All Weights Updating-LVQ (AWU-LVQ) algorithm and the Neighbouring Weights Updating-LVQ (NWU-LVQ) algorithm, yielding an "intelligent" diagnostic heart system with higher accuracy and reduced training time compared to existing AI techniques.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Visual region understanding: unsupervised extraction and abstraction
The ability to gain a conceptual understanding of the world in uncontrolled environments is the ultimate goal of vision-based computer systems. Technological
societies today are heavily reliant on surveillance and security infrastructure, robotics, medical image analysis, visual data categorisation and search, and smart device user interaction, to name a few. Out of all the complex problems tackled
by computer vision today in context of these technologies, that which lies closest to the original goals of the field is the subarea of unsupervised scene analysis or scene modelling. However, its common use of low level features does not provide
a good balance between generality and discriminative ability, both a result and a symptom of the sensory and semantic gaps existing between low level computer
representations and high level human descriptions.
In this research we explore a general framework that addresses the fundamental
problem of universal unsupervised extraction of semantically meaningful visual
regions and their behaviours. For this purpose we address issues related to
(i) spatial and spatiotemporal segmentation for region extraction, (ii) region shape modelling, and (iii) the online categorisation of visual object classes and the spatiotemporal analysis of their behaviours. Under this framework we propose (a)
a unified region merging method and spatiotemporal region reduction, (b) shape
representation by the optimisation and novel simplication of contour-based growing neural gases, and (c) a foundation for the analysis of visual object motion properties using a shape and appearance based nearest-centroid classification algorithm
and trajectory plots for the obtained region classes.
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Specifically, we formulate a region merging spatial segmentation mechanism
that combines and adapts features shown previously to be individually useful,
namely parallel region growing, the best merge criterion, a time adaptive threshold, and region reduction techniques. For spatiotemporal region refinement we
consider both scalar intensity differences and vector optical flow. To model the shapes of the visual regions thus obtained, we adapt the growing neural gas for
rapid region contour representation and propose a contour simplication technique. A fast unsupervised nearest-centroid online learning technique next groups observed region instances into classes, for which we are then able to analyse spatial
presence and spatiotemporal trajectories. The analysis results show semantic correlations to real world object behaviour. Performance evaluation of all steps across
standard metrics and datasets validate their performance
Computerised electrocardiogram classification
Advances in computing have resulted in many engineering processes being automated. Electrocardiogram (ECG) classification is one such process. The analysis of ECGs can benefit from the wide availability and power of modern computers. This study presents the usage of computer technology in the field of computerised ECG classification. Computerised electrocardiogram classification can help to reduce healthcare costs by enabling suitably equipped general practitioners to refer to hospital only those people with serious heart problems. Computerised ECG classification can also be very useful in shortening hospital waiting lists and saving life by discovering heart diseases early. The thesis investigates the automatic classification of ECGs into different disease categories using Artificial Intelligence (AI) techniques. A comparison of the use of different feature sets and AI classifiers is presented. The feature sets include conventional cardiological features, as well as features taken directly from time domain samples of an ECG. The benchmark AI classifiers tested include those based on neural network, k-Nearest Neighbour and inductive learning techniques. The research proposes two modifications to the learning vector quantisation (LVQ) neural network, namely the All Weights Updating-LVQ (AWU-LVQ) algorithm and the Neighbouring Weights Updating-LVQ (NWU-LVQ) algorithm, yielding an "intelligent" diagnostic heart system with higher accuracy and reduced training time compared to existing AI techniques
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