9 research outputs found

    Efficient Human Activity Recognition in Large Image and Video Databases

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    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

    Support matrix machine: A review

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    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)

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    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

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    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

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    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
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