9 research outputs found
Efficient Human Activity Recognition in Large Image and Video Databases
Vision-based human action recognition has attracted considerable interest in recent research for its applications to video surveillance, content-based search, healthcare, and interactive games. Most existing research deals with building informative feature descriptors, designing efficient and robust algorithms, proposing versatile and challenging datasets, and fusing multiple modalities. Often, these approaches build on certain conventions such as the use of motion cues to determine video descriptors, application of off-the-shelf classifiers, and single-factor classification of videos. In this thesis, we deal with important but overlooked issues such as efficiency, simplicity, and scalability of human activity recognition in different application scenarios: controlled video environment (e.g.~indoor surveillance), unconstrained videos (e.g.~YouTube), depth or skeletal data (e.g.~captured by Kinect), and person images (e.g.~Flicker). In particular, we are interested in answering questions like (a) is it possible to efficiently recognize human actions in controlled videos without temporal cues? (b) given that the large-scale unconstrained video data are often of high dimension low sample size (HDLSS) nature, how to efficiently recognize human actions in such data? (c) considering the rich 3D motion information available from depth or motion capture sensors, is it possible to recognize both the actions and the actors using only the motion dynamics of underlying activities? and (d) can motion information from monocular videos be used for automatically determining saliency regions for recognizing actions in still images
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A Novel Separating Hyperplane Classification Framework to Unify Nearest-class-model Methods for High-dimensional Data
In this paper, we establish a novel separating hyperplane classification (SHC) framework to unify three nearest-classmodel methods for high-dimensional data: the nearest subspace method (NSM), the nearest convex hull method (NCHM) and the nearest convex cone method (NCCM). Nearest-class-model methods are an important paradigm for classification of highdimensional data. We first introduce the three nearest-classmodel methods and then conduct dual analysis for theoretically investigating them, to understand deeply their underlying classification mechanisms. A new theorem for the dual analysis of NCCM is proposed in this paper, through discovering the relationship between a convex cone and its polar cone. We then establish the new SHC framework to unify the nearest-classmodel methods based on the theoretical results. One important application of this new SHC framework is to help explain empirical classification results: why one class model has better performance than others on certain datasets. Finally, we propose a new nearest-class-model method, the soft NCCM, under the novel SHC framework to solve the overlapping class model problem. For illustrative purposes, we empirically demonstrate the significance of our SHC framework and the soft NCCM through two types of typical real-world high-dimensional data, the spectroscopic data and the face image data
Support matrix machine: A review
Support vector machine (SVM) is one of the most studied paradigms in the
realm of machine learning for classification and regression problems. It relies
on vectorized input data. However, a significant portion of the real-world data
exists in matrix format, which is given as input to SVM by reshaping the
matrices into vectors. The process of reshaping disrupts the spatial
correlations inherent in the matrix data. Also, converting matrices into
vectors results in input data with a high dimensionality, which introduces
significant computational complexity. To overcome these issues in classifying
matrix input data, support matrix machine (SMM) is proposed. It represents one
of the emerging methodologies tailored for handling matrix input data. The SMM
method preserves the structural information of the matrix data by using the
spectral elastic net property which is a combination of the nuclear norm and
Frobenius norm. This article provides the first in-depth analysis of the
development of the SMM model, which can be used as a thorough summary by both
novices and experts. We discuss numerous SMM variants, such as robust, sparse,
class imbalance, and multi-class classification models. We also analyze the
applications of the SMM model and conclude the article by outlining potential
future research avenues and possibilities that may motivate academics to
advance the SMM algorithm
Reinforcing Soft Independent Modelling of Class Analogy (SIMCA)
Soft independent modelling of class analogy (SIMCA) is a widely used subspacebased
classification technique for spectral data analysis. The principal component
(PC) subspace is built for each class separately through principal components analysis
(PCA). The squared orthogonal distance (OD2) between the test sample and the
class subspace of each class, and the squared score distance (SD2) between the projection
of the test sample to the class subspace and the centre of the class subspace,
are usually used in the classification rule of SIMCA to classify the test sample.
Although it is commonly used to classify high-dimensional spectral data,
SIMCA suffers from several drawbacks and some misleading calculations in literature.
First, modelling classes separately makes the discriminative between-class
information neglected. Second, the literature of SIMCA fail to explore the potential
benefit of using geometric convex class models, whose superior classification
performance has been demonstrated in face recognition. Third, based on our experiments
on several real datasets, calculating OD2 using the formulae in a highlycited
SIMCA paper (De Maesschalck et al., 1999) results in worse classification
performance than using those in the original SIMCA paper (Wold, 1976) for some
high-dimensional data and provides misleading classification results. Fourth, the
distance metrics used in the classification rule of SIMCA are predetermined, which
are not adapted to different data.
Hence the research objectives of my PhD work are to reinforce SIMCA from
the following four perspectives: O1) to make its feature space more discriminative;
O2) to use geometric convex models as class models in SIMCA for spectral data
classification and to study the classification mechanism of classification using different class models; O3) to investigate the equality and inequality of the calculations
of OD2 in De Maesschalck et al. (1999) and Wold (1976) for low-dimensional and
high-dimensional scenarios; and O4) to make its distance metric adaptively learned
from data. In this thesis, we present four contributions to achieve the above four
objectives, respectively:
First, to achieve O1), we propose to first project the original data to a more
discriminative subspace before applying SIMCA. To build such discriminative subspace,
we propose the discriminatively ordered subspace (DOS) method, which
selects the eigenvectors of the generating matrix with high discriminative ability
between classes to span DOS. A paper of this work, “Building a discriminatively
ordered subspace on the generating matrix to classify high-dimensional spectral
data”, has been recently published by the journal of “Information Sciences”.
Second, to achieve O2), we use the geometric convex models, convex hull and
convex cone, as class models in SIMCA to classify spectral data. We study the dual
of classification methods using three class models: the PC subspace, convex hull
and convex cone, to investigate their classification mechanism. We provide theoretical
results of the dual analysis, establish a separating hyperplane classification
(SHC) framework and provide a new data exploration scheme to analyse the properties
of a dataset and why such properties make one or more of the methods suitable
for the data.
Third, to achieve O3), we compare the calculations of OD2 in De Maesschalck
et al. (1999) and Wold (1976). We show that the corresponding formulae in the two
papers are equivalent, only when the training data of one class have more samples
than features. When the training data of one class have more features than samples
(i.e. high-dimensional), the formulae in De Maesschalck et al. (1999) are not precise
and affect the classification results. Hence we suggest to use the formulae in Wold
(1976) to calculate OD2, to get correct classification results of SIMCA for highdimensional
data.
Fourth, to achieve O4), we learn the distance metrics in SIMCA based on the
derivation of a general formulation of the classification rules used in literature. We define the general formulation as the distance metric from a sample to a class subspace.
We propose the method of learning distance to subspace to learn this distance
metric by making the samples to be closer to their correct class subspaces while be
farther away from their wrong class subspaces.
Lastly, at the end of this thesis we append two pieces of work on hyperspectral
image analysis. First, the joint paper with Mr Mingzhi Dong and Dr Jing-Hao Xue,
“Spectral Nonlocal Restoration of Hyperspectral Images with Low-Rank Property”,
has been published by the IEEE Journal of Selected Topics in Applied Earth Observations
and Remote Sensing. Second, the joint paper with Dr Fei Zhou and Dr
Jing-Hao Xue, “MvSSIM: A Quality Assessment Index for Hyperspectral Images”,
has been in revision for Neurocomputing. As these two papers do not focus on the
research objectives of this thesis, they are appended as some additional work during
my PhD study
Nearest hyperdisk methods for high-dimensional classification
International audienceIn high-dimensional classification problems it is infeasible to include enough training samples to cover the class regions densely. Irregularities in the resulting sparse sample distributions cause local classifiers such as Nearest Neighbors (NN) and kernel methods to have irregular decision boundaries. One solution is to "fill in the holes" by building a convex model of the region spanned by the training samples of each class and classifying examples based on their distances to these approximate models. Methods of this kind based on affine and convex hulls and bounding hyperspheres have already been studied. Here we propose a method based on the bounding hyperdisk of each class - the intersection of the affine hull and the smallest bounding hypersphere of its training samples. We argue that in many cases hyperdisks are preferable to affine and convex hulls and hyperspheres: they bound the classes more tightly than affine hulls or hyperspheres while avoiding much of the sample overfitting and computational complexity that is inherent in high-dimensional convex hulls. We show that the hyperdisk method can be kernelized to provide nonlinear classifiers based on non-Euclidean distance metrics. Experiments on several classification problems show promising results
Nearest Hyperdisk Methods for High-Dimensional Classification
In high-dimensional classification problems it is infeasible to include enough training samples to cover the class regions densely. Irregularities in the resulting sparse sample distributions cause local classifiers such as Nearest Neighbors (NN) and kernel methods to have irregular decision boundaries. One solution is to “fill in the holes” by building a convex model of the region spanned by the training samples of each class and classifying examples based on their distances to these approximate models. Methods of this kind based on affine and convex hulls and bounding hyperspheres have already been studied. Here we propose a method based on the bounding hyperdisk of each class – the intersection of the affine hull and the smallest bounding hypersphere of its training samples. We argue that in many cases hyperdisks are preferable to affine and convex hulls and hyperspheres: they bound the classes more tightly than affine hulls or hyperspheres while avoiding much of the sample overfitting and computational complexity that is inherent in high-dimensional convex hulls. We show that the hyperdisk method can be kernelized to provide nonlinear classifiers based on non-Euclidean distance metrics. Experiments on several classification problems show promising results. 1