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Machine Learning (ML) approaches are widelyused classification/regression methods for data mining applications. However, the time-consuming training process greatly limits the efficiency of ML approaches. We use the example of SVM (traditional ML algorithm) and DNN (state-of-the-art ML algorithm) to illustrate the idea in this paper. For SVM, a major performance bottleneck of current tools is that...
With the fast development of various methods for image classification using the bag-of-features model, machines can efficiently classify images by image content. Spatial pyramid matching (SPM) for sparse coding to create the dictionary is a popular and very well performing approach for image classification. The linear SPM was proposed to take advantage of the speed of the linear Support Vector Machine...
We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency. The subset of filters is adaptively selected by a deep attentional network according to the dynamic properties of the tracking target. Our contributions are manifold, and are summarised as follows: (i) Introducing the Attentional...
Principal component analysis (PCA) is one of the most versatile tools for unsupervised learning with applications ranging from dimensionality reduction to exploratory data analysis and visualization. While much effort has been devoted to encouraging meaningful representations through regularization (e.g. non-negativity or sparsity), underlying linearity assumptions can limit their effectiveness. To...
Person Re-identification (ReID) is to identify the same person across different cameras. It is a challenging task due to the large variations in person pose, occlusion, background clutter, etc. How to extract powerful features is a fundamental problem in ReID and is still an open problem today. In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn powerful features over full...
Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases. In this paper, we introduce a deep transfer learning scheme, called selective joint fine-tuning, for improving the performance of deep learning tasks with insufficient training data. In this scheme, a target learning task...
To overcome the limitations of manual features and obtain the operating characteristics of the equipment in complex operation processes, different deep learning models have been utilized for industrial data, improving classification accuracy yet causing some other limitations meanwhile. In this paper, a deep hybrid model named Stochastic Convolutional and Deep Belief Network (SCDBN), which assembles...
Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and achieved effective performance. In general, the previous networks are not enough deep, which might not extract very discriminant features for classification. In addition, they do not consider strong correlations among different hierarchical layers. Due to the two problems, a hybrid deep residual network is presented...
Deep learning techniques have brought in revolutionary achievements for feature learning of images. In this paper, a novel structure of 3-Dimensional Convolutional AutoEncoder (3D-CAE) is proposed for hyperspectral spatial-spectral feature learning, in which the spatial context is considered by constructing a 3-Dimensional input using pixels in a spatial neighborhood. All the parameters involved in...
Image processing plays a vital role in the early detection and diagnosis of Hepatocellular Carcinoma (HCC). In this paper, we present a computational intelligence based Computer-Aided Diagnosis (CAD) system that helps medical specialists detect and diagnose HCC in its initial stages. The proposed CAD comprises the following stages: image enhancement, liver segmentation, feature extraction and characterization...
Effective detection and discrimination of surface deformation features in Synthetic Aperture Radar imagery is one of the most important applications of the data. Areas that undergo surface deformation can pose health and safety risks which necessitates an automatic and reliable means of surface deformation discrimination. Due to the similarities between subsidence features and false positives, advanced...
Deep neural networks can learn deep feature representation for hyperspectral image (HSI) interpretation and achieve high classification accuracy in different datasets. However, counterintuitively, the classification performance of deep learning models degrades as their depth increases. Therefore, we add identity mappings to convolutional neural networks for every two convolutional layers to build...
Convolutional neural networks (CNNs), widely studied in the domain of computer vision, are more recently finding application in the analysis of high-resolution aerial and satellite imagery. In this paper, we investigate a deep feature learning approach based on CNNs for the detection of informal settlements in Dar es Salaam, Tanzania. This information is vital for decision making and planning of upgrading...
Convolutional Neural Networks (CNNs) are responsible for major breakthroughs in object recognition in still images. This work presents an end to end very deep architecture with small convolutional kernel size, small convolutional strides and very deep network architecture for person re-identification in video streams. To achieve such system several good practices for the training were tested, namely:...
Support Vector Machine (SVM) is one of the most popular machine learning algorithm to perform classification tasks and help organizations in different ways to improve their efficiency. A lot of studies have been made to improve SVM including speed, accuracy, and/or scalability. The algorithm possesses parameters that need precision tuning to perform well. This work proposes a novel parallelized parameter...
Steganographer detection problem is to identify culprit actors, who try to hide confidential information with steganography, among many innocent actors. This task has significant challenges, including various embedding steganographic algorithms and payloads, which are usually avoided in steganalysis under laboratory conditions. In this paper, we propose a novel steganographer detection model based...
We present a novel deep learning framework for crowd counting by learning a perspective-embedded deconvolution network. Perspective is an inherent property of most surveillance scenes. Unlike the traditional approaches that exploit the perspective as a separate normalization, we propose to fuse the perspective into a deconvolution network, aiming to obtain a robust, accurate and consistent crowd density...
Support Vector Machine (SVM) is one of the most popular machine learning algorithms. An energy-efficient SVM classifier is proposed in this paper, where approximate computing is utilized to reduce energy consumption and silicon area. A hardware architecture with reconfigurable kernels and overflow-resilient limiter is presented. For different applications, different kernels can be chosen and configured...
Lightweight convolutional neural network (CNN) on tiny embedded platforms can offer energy efficient solution for today's IoT devices. However, CNN implementation on embedded system faces processing bottleneck in convolutional layers and memory storage issues in fully connected layers. In past years, heterogeneous acceleration, where compute intensive tasks are performed on kernel specific cores,...
The problem of preference functions model development for multiple criteria decision-making is considered based on machine-learning approach. It is assumed that the training sample for a plurality of objects, for which decisions are made, is formed from a set of measured features or the particular criteria and the matrix of pairwise comparisons. The problem of constructing a linear preference function...
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