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Graph algorithms have wide applicablity to a variety of domains and are often used on massive datasets. Recent standardization efforts such as the GraphBLAS specify a set of key computational kernels that hardware and software developers can adhere to. Graphulo is a processing framework that enables GraphBLAS kernels in the Apache Accumulo database. In our previous work, we have demonstrated a core...
The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms. Current random projection methods avoid the curse of dimensionality by embedding the nonlinear feature space into a low dimensional Euclidean space to create nonlinear kernels. We introduce a Layered Random...
Localizing heavily occluded human faces is a challenging problem in facial detection. Previous methods mainly employ sliding windows by determining whether windows include human faces. In this paper, we provide a novel segmentation-based perspective for heavily occluded face localization with deep convolutional neural networks (CNN). Our model takes an image as input without complicated pre-processing...
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Convolutional neural networks (CNN) have been successfully applied to image super-resolution (SR) as well as other image restoration tasks. In this paper, we consider the problem of compressed video super-resolution. Traditional SR algorithms for compressed videos rely on information from the encoder such as frame type or quantizer step, whereas our algorithm only requires the compressed low resolution...
Exemplar-based methods have shown their potential in synthesizing novel but visually plausible contents for image super-resolution (SR), by using the implicit knowledge conveyed by the exemplar database. In practice, however, it is common that unwanted artifacts and low quality results are produced due to the using of inappropriate exemplars. How are the “right” exemplars defined and identified? This...
In this paper, an improved Kernel Fisher Discriminant (KFD) method is used in face recognition. A Generalized Kernel Fisher Discriminant Analysis (GKFD) is proposed to make the most of two kinds of discriminant information in “double discriminant subspaces”. It can also uniform the discriminant functions in two subspaces of DSDA. Experimental results on ORL face database show the feasibility of the...
Movement classification from electromyography (EMG) signals is a promising vector for improvement of human computer interaction and prosthetic control. Conventional work in this area typically makes use of expert knowledge to select a set of movements a priori and then design classifiers based around these movements. The disadvantage of this approach is that different individuals might have different...
In the vision of the intelligent factory of industry 4.0, many enterprises are eager to increase the productivity by analysing the production data collected from equipment in runtime or products. Developing a continuous yield improvement system framework with big data capability shall gain an insight into the demands of continuous yield improvement. In this paper, we developed a cyber-physical-style...
In this paper, a robust technique to construct feature vector for gender classification has been proposed. Discrete Wavelet transform is used in concatenation with Discrete Cosine transform to form the feature vector. Initially, multi-level Discrete Wavelet transform is applied to images to obtain the approximation coefficients of image. Discrete Cosine transform are then calculated for the obtained...
In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both...
Face alignment is very crucial to the task of face attributes recognition. The performance of face attributes recognition would notably degrade if the fiducial points of the original face images are not precisely detected due to large lighting, pose and occlusion variations. In order to alleviate this problem, we propose a spatial transform based deep CNNs to improve the performance of face attributes...
In this paper, we propose a feature selection and representation combination method to generate discriminative features for speech emotion recognition. In feature selection stage, a Multiple Kernel Learning (MKL) based strategy is used to obtain the optimal feature subset. Specifically, features selected at least n times among 10-fold cross validation are collected to build a new feature subset named...
Many diseases can be detected via performing the electrocardiogram diagnosis. Although the electrocardiograms can be acquired in the public clinics, hospitals or even at homes by using the existing devices, these existing devices are not tiny enough to carry on the bodies. Hence, it is difficult to acquire the electrocardiograms all the time. In order to acquire the electrocardiograms all the time,...
This paper proposes a supervised feature extraction approach which is capable to select distinctive features for the recognition of human gait under clothing and carrying conditions thus improving the recognition performances. The principle of the suggested approach is based on the use of feature texture descriptors extracted from Gait Energy Image (GEI). The proposed features are computed using the...
Automated blood vessel segmentation of retinal images offers huge potential benefits for medical diagnosis of different ocular diseases. In this paper, 2D Matched Filters (MF) are applied to fundus retinal images to detect vessels which are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Due to the Gaussian nature of blood vessel profile, the MF with Gaussian kernel often...
This paper presents a model of Pulse-Coupled Neural Network (PCNN) for multispectral image segmentation. Its application for license plate recognition (LPR) is considered; this consists of three processing steps. First step extracts the license plate coordinates from the original image; second step is the PCNN-based segmentation method to obtain a binary image containing only the characters of the...
Handwritten character recognition systems suffers from different training and testing sets distributions. In this paper, we propose a two-step domain adaptive multiple kernel learning algorithm, which learns a kernel function based on several kernels in the first step, and trains a target classifier by applying the learned kernel in the second step. Our method can be employed both in semi-supervised...
A new technique to construct feature vector for gender classification is proposed in this paper. Here, new feature reduction technique is used to remove the irrelevant features of images. Feature reduction also helps in reducing the over fitting problem of the dataset. KPCA is a kernel based PCA which maps data from original space to non-linear feature space. Kernel trick helps in reducing the expensive...
In this paper, a new technique for constructing feature vector from DCT coefficients for gender classification has been presented. Firstly, images are divided into 8 × 8 sub images. DCT coefficients are calculated for each block in image. New technique is used for constructing the feature vector from DCT coefficients. Finally, SVM with Rbf kernel is used for classifying the images into male and female...
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