51 research outputs found

    Deep invariant feature learning for remote sensing scene classification

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    Image classification, as the core task in the computer vision field, has proceeded at a break­neck pace. It largely attributes to the recent growth of deep learning techniques which have blown the conventional statistical methods on a plethora of benchmarks and even can outperform humans in specific image classification tasks. Despite deep learning exceeding alternative techniques, they have many apparent disadvantages that prevent them from being deployed for the general-purpose. Specifically, deep learning always requires a considerable amount of well-annotated data to circumvent the problems of over-fitting and the lacking of prior knowledge. However, manually labelled data is expensive to acquire and is impossible to incorporate the variations as much as the real world. Consequently, deep learning models usually fail when they confront with the underrepresented variations in the training data. This is the main reason why the deep learning model is barely satisfactory in the challeng­ing image recognition task that contains nuisance variations such as, Remote Sensing Scene Classification (RSSC). The classification of remote sensing scene image is a procedure of assigning the seman­tic meaning labels for the given satellite images that contain the complicated variations, such as texture and appearances. The algorithms for effectively understanding and recognising remote sensing scene images have the potential to be employed in a broad range of applications, such as urban planning, Land Use and Land Cover (LULC) determination, natural hazards detection, vegetation mapping, environmental monitoring. This inspires us to de­sign the frameworks that can automatically predict the precise label for satellite images. In our research project, we mine and define the challenges in RSSC community compared with general scene image recognition tasks. Specifically, we summarise the problems into the following perspectives. 1) Visual-semantic ambiguity: the discrepancy between visual features and semantic concepts; 2) Variations: the intra-class diversity and inter-class similarity; 3) Clutter background; 4) The small size of the training set; 5) Unsatisfactory classification accuracy in large-scale datasets. To address the aforementioned challenges, we explore a way to dynamically expand the capabilities of incorporating the prior knowledge by transforming the input data so that we can learn the globally invariant second-order features from the transformed data for improving the performance of RSSC tasks. First, we devise a recurrent transformer network (RTN) to progressively discover the discriminative regions of input images and learn the corresponding second-order features. The model is optimised using pairwise ranking loss to achieve localising discriminative parts and learning the corresponding features in a mutu­ally reinforced way. Second, we observed that existing remote sensing image datasets lack the provision of ontological structures. Therefore, a multi-granularity canonical appearance pooling (MG-CAP) model is proposed to automatically seek the implied hierarchical structures of datasets and produced covariance features contained the multi-grained information. Third, we explore a way to improve the discriminative power of the second-order features. To accomplish this target, we present a covariance feature embedding (CFE) model to im­prove the distinctive power of covariance pooling by using suitable matrix normalisation methods and a low-norm cosine similarity loss to accurately metric the distances of high­dimensional features. Finally, we improved the performance of RSSC while using fewer model parameters. An invariant deep compressible covariance pooling (IDCCP) model is presented to boost the classification accuracy for RSSC tasks. Meanwhile, we proofed the generalisability of our IDCCP model using group theory and manifold optimisation techniques. All of the proposed frameworks allow being optimised in an end-to-end manner and are well-supported by GPU acceleration. We conduct extensive experiments on the well-known remote sensing scene image datasets to demonstrate the great promotions of our proposed methods in comparison with state-of-the-art approaches

    Statistical and Dynamical Modeling of Riemannian Trajectories with Application to Human Movement Analysis

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    abstract: The data explosion in the past decade is in part due to the widespread use of rich sensors that measure various physical phenomenon -- gyroscopes that measure orientation in phones and fitness devices, the Microsoft Kinect which measures depth information, etc. A typical application requires inferring the underlying physical phenomenon from data, which is done using machine learning. A fundamental assumption in training models is that the data is Euclidean, i.e. the metric is the standard Euclidean distance governed by the L-2 norm. However in many cases this assumption is violated, when the data lies on non Euclidean spaces such as Riemannian manifolds. While the underlying geometry accounts for the non-linearity, accurate analysis of human activity also requires temporal information to be taken into account. Human movement has a natural interpretation as a trajectory on the underlying feature manifold, as it evolves smoothly in time. A commonly occurring theme in many emerging problems is the need to \emph{represent, compare, and manipulate} such trajectories in a manner that respects the geometric constraints. This dissertation is a comprehensive treatise on modeling Riemannian trajectories to understand and exploit their statistical and dynamical properties. Such properties allow us to formulate novel representations for Riemannian trajectories. For example, the physical constraints on human movement are rarely considered, which results in an unnecessarily large space of features, making search, classification and other applications more complicated. Exploiting statistical properties can help us understand the \emph{true} space of such trajectories. In applications such as stroke rehabilitation where there is a need to differentiate between very similar kinds of movement, dynamical properties can be much more effective. In this regard, we propose a generalization to the Lyapunov exponent to Riemannian manifolds and show its effectiveness for human activity analysis. The theory developed in this thesis naturally leads to several benefits in areas such as data mining, compression, dimensionality reduction, classification, and regression.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

    Representation Learning via Manifold Flattening and Reconstruction

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    This work proposes an algorithm for explicitly constructing a pair of neural networks that linearize and reconstruct an embedded submanifold, from finite samples of this manifold. Our such-generated neural networks, called Flattening Networks (FlatNet), are theoretically interpretable, computationally feasible at scale, and generalize well to test data, a balance not typically found in manifold-based learning methods. We present empirical results and comparisons to other models on synthetic high-dimensional manifold data and 2D image data. Our code is publicly available.Comment: 44 pages, 19 figure

    Reflection positivity and invertible topological phases

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    We implement an extended version of reflection positivity (Wick-rotated unitarity) for invertible topological quantum field theories and compute the abelian group of deformation classes using stable homotopy theory. We apply these field theory considerations to lattice systems, assuming the existence and validity of low energy effective field theory approximations, and thereby produce a general formula for the group of Symmetry Protected Topological (SPT) phases in terms of Thom's bordism spectra; the only input is the dimension and symmetry group. We provide computations for fermionic systems in physically relevant dimensions. Other topics include symmetry in quantum field theories, a relativistic 10-fold way, the homotopy theory of relativistic free fermions, and a topological spin-statistics theorem.Comment: 136 pages, 16 figures; minor changes/corrections in version 2; v3 major revision; v4 minor revision: corrected proof of Lemma 9.55, many small changes throughout; v5 version for publication in Geometry & Topolog
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