30 research outputs found
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods
Feature extraction and dimensionality reduction are important tasks in many
fields of science dealing with signal processing and analysis. The relevance of
these techniques is increasing as current sensory devices are developed with
ever higher resolution, and problems involving multimodal data sources become
more common. A plethora of feature extraction methods are available in the
literature collectively grouped under the field of Multivariate Analysis (MVA).
This paper provides a uniform treatment of several methods: Principal Component
Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis
(CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions
derived by means of the theory of reproducing kernel Hilbert spaces. We also
review their connections to other methods for classification and statistical
dependence estimation, and introduce some recent developments to deal with the
extreme cases of large-scale and low-sized problems. To illustrate the wide
applicability of these methods in both classification and regression problems,
we analyze their performance in a benchmark of publicly available data sets,
and pay special attention to specific real applications involving audio
processing for music genre prediction and hyperspectral satellite images for
Earth and climate monitoring
Face Recognition: Issues, Methods and Alternative Applications
Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the 1960s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. In this chapter, we have discussed face recognition processing, including major components such as face detection, tracking, alignment and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition
Investigation of feature extraction algorithms and techniques for hyperspectral images.
Doctor of Philosophy (Computer Engineering). University of KwaZulu-Natal. Durban, 2017.Hyperspectral images (HSIs) are remote-sensed images that are characterized
by very high spatial and spectral dimensions and nd applications, for example,
in land cover classi cation, urban planning and management, security and food
processing. Unlike conventional three bands RGB images, their high
dimensional data space creates a challenge for traditional image processing
techniques which are usually based on the assumption that there exists
su cient training samples in order to increase the likelihood of high
classi cation accuracy. However, the high cost and di culty of obtaining
ground truth of hyperspectral data sets makes this assumption unrealistic and
necessitates the introduction of alternative methods for their processing.
Several techniques have been developed in the exploration of the rich spectral
and spatial information in HSIs. Speci cally, feature extraction (FE)
techniques are introduced in the processing of HSIs as a necessary step before
classi cation. They are aimed at transforming the high dimensional data of the
HSI into one of a lower dimension while retaining as much spatial and/or
spectral information as possible. In this research, we develop semi-supervised
FE techniques which combine features of supervised and unsupervised
techniques into a single framework for the processing of HSIs. Firstly, we
developed a feature extraction algorithm known as Semi-Supervised Linear
Embedding (SSLE) for the extraction of features in HSI. The algorithm
combines supervised Linear Discriminant Analysis (LDA) and unsupervised
Local Linear Embedding (LLE) to enhance class discrimination while also
preserving the properties of classes of interest. The technique was developed
based on the fact that LDA extracts features from HSIs by discriminating
between classes of interest and it can only extract C 1 features provided there
are C classes in the image by extracting features that are equivalent to the
number of classes in the HSI. Experiments show that the SSLE algorithm
overcomes the limitation of LDA and extracts features that are equivalent to
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the number of classes in HSIs. Secondly, a graphical manifold dimension
reduction (DR) algorithm known as Graph Clustered Discriminant Analysis
(GCDA) is developed. The algorithm is developed to dynamically select labeled
samples from the pool of available unlabeled samples in order to complement
the few available label samples in HSIs. The selection is achieved by entwining
K-means clustering with a semi-supervised manifold discriminant analysis.
Using two HSI data sets, experimental results show that GCDA extracts
features that are equivalent to the number of classes with high classi cation
accuracy when compared with other state-of-the-art techniques. Furthermore,
we develop a window-based partitioning approach to preserve the spatial
properties of HSIs when their features are being extracted. In this approach,
the HSI is partitioned along its spatial dimension into n windows and the
covariance matrices of each window are computed. The covariance matrices of
the windows are then merged into a single matrix through using the Kalman
ltering approach so that the resulting covariance matrix may be used for
dimension reduction. Experiments show that the windowing approach achieves
high classi cation accuracy and preserves the spatial properties of HSIs. For
the proposed feature extraction techniques, Support Vector Machine (SVM)
and Neural Networks (NN) classi cation techniques are employed and their
performances are compared for these two classi ers. The performances of all
proposed FE techniques have also been shown to outperform other
state-of-the-art approaches