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For face recognition systems, impostors can obtain legal identity authentication by presenting the printed images, the downloaded images or candid videos to the sensor. In this paper, an enhanced face local binary feature (ELBP) of a face map is extracted as a classification feature to identify whether the face map is a real face or a fake face. Compared with the dynamic or static methods proposed...
This paper aims to develop an effective flower classification approach using the technology of feature extraction. With this regard, a fused descriptor based on Pyramid Histogram of Visual Words (PHOW) is used to extract the color, texture and contour information of flower image. Secondly, Dictionary Learning and Locality-constrained Linear Coding (LLC) are operated on PHOW feature and then images...
Most existing weakly supervised localization (WSL) approaches learn detectors by finding positive bounding boxes based on features learned with image-level supervision. However, those features do not contain spatial location related information and usually provide poor-quality positive samples for training a detector. To overcome this issue, we propose a deep self-taught learning approach, which makes...
An efficient recognition framework requires both good feature representation and effective classification methods. This paper proposes such a framework based on a spatial Scale Invariant Feature Transform (SIFT) combined with a logistic regression classifier. The performance of the proposed framework is compared to that of state-of-the-art methods based on the Histogram of Orientation Gradients, SIFT...
In this paper, a novel kernel low rank representation (KLRR) method for hyperspectral image classification is proposed. Firstly, we extract the global structure characteristics information of the hyperspectral image based on low rank representation (LRR), then use it as a prior to constrain the recovery coefficient matrix. In order to further improve the classification efficiency and deal with the...
The very high resolution (VHR) images can be seen as multiview data. For better organizing and highlighting similarities and differences between the multiple views of data, a semisupervised multiview feature selection (SemiMFS) method is proposed in this paper, based on consensus and complementary principles. In SemiMFS, feature views are generated by decomposing features into multiple disjoint and...
Software defined radio (SDR) plays an important role in military and commerce because of its inherent flexibility. It also has great potential in home use, and it can be controlled by software installed on a personal computer or embedded system to achieve different purposes. In this paper, a novel method of dynamic gesture recognition based on support vector machine (SVM) and SDR is proposed. It is...
Wi-Fi signals have been typically acting as information carriers in modern communication system, but recent research has revealed their powerful capability in detecting and identifying various targets. With Wi-Fi, we can now "see" people's location, activity, and even hand gestures. In this paper, a new method of dynamic gesture recognition using Wi-Fi based on signal processing and machine...
Pedestrian detection exhibits important application value in driver assistance systems, The detection performance often suffers from the various appearances of pedestrians, the illumination changes and complex background. Aiming at solving these challenges, in this paper, first, a new color moments feature is presented to describe the local similarity structure of pedestrians, which reduces the influence...
Face recognition is a research hotspot in recent years. In order to improve recognition accuracy of face recognition, a feature selection method for face image based on Gabor feature and recursive feature elimination was proposed in this paper. Firstly, Gabor features were extracted from face image. Then, face image was divided into pieces and Gabor feature statistics of these pieces were linked in...
Automatic analysis of histopathological images has been widely investigated using computational image processing and machine learning techniques. Computer-aided diagnosis (CAD) systems and content-based image retrieval (CBIR) systems have been successfully developed for diagnosis, disease detection, and decision support in this area. In this paper, we focus on a scalable image retrieval method with...
To help the biomedical scientist pre-confirm the disease-related genes, we considered these gene as a whole research set and analyzed the topological features of their interaction network. Two strategies had been proposed to construct the disease-related gene network from the OMIM database. Using these two constructed sets, we trained two support vector machine prediction models, the accuracy of which...
Existing Automatic Image Annotation (AIA) systems are typically developed, trained and tested using high quality, manually labelled images. The tremendous manual efforts required with an untested ability to scale and tolerate noise all have an impact on existing systems' applicability to real-world data. In this paper, we propose a novel AIA system which harnesses the collective intelligence on the...
According to the symmetric characteristics of bispectrum, a novel feature extraction scheme, which includes the summation-at-every-column feature vector, the summation-at-every-row feature vector and their combination in a triangle area, one of the 12 symmetric areas of bispectrum, is proposed. By using One-against-One (OAO) method of multi classification of Support Vector Machine (SVM), the mean...
Support vector machine is a machine learning method which is based on structural risk minimization principle. The traditional parameter optimization methods of support vector regression mainly employ grid search method and so on. These methods have shortcomings of being guided by human experience and time-consuming. In recent years, many intelligent search algorithms are used for SVR parameter optimization...
Pedestrian detection by camera sensor is an important function in intelligent vehicle. Histograms of Oriented Gradients (HOG) features is a kind of efficient pedestrian feature. We optimized the HOG features to achieve an accurate human detection system. We don't normalize the input detection windows but resize the cell and block by same ratio. In the processing of calculate the HOG features, the...
Accurate grid resources prediction is crucial for a grid scheduler. In this study, support vector regression (SVR), which is an effective regression algorithm, is applied to grid resource prediction. In order to obtain better prediction performance, SVR's parameters must be selected carefully. Therefore, a particle swarm optimization-based SVR (PSO-SVR) model, in which PSO is used to determine free...
Traffic can reflect the latent rules and characteristics of the wireless network. Through researching, we found that the more accurate traffic prediction, the higher efficiency, utilization rate of network bandwidth and QoS can be guaranteed. Therefore, how to construct predictive models of wireless network traffic exactly is a major research topic. In this paper, Minimax Probability Machine Regression...
Accurate identification of P2P traffic makes great sense for efficient network management and reasonable utility of network resources. P2P traffic identification has two promising approaches: DPI (deep packet inspection) and DFI (deep flow inspection). The purpose of the paper is to solve how to carry out research on how to take advantage of merits of these methods, avoid their defects, and assemble...
P2P traffic has become one of the most significant portions of the network traffic. How to improve the accuracy of the traffic identification efficiently is still a difficult problem. A promising approach that has recently received some attention is traffic classification using machine learning techniques. In this paper, we propose a BP neural network algorithm for P2P traffic classification problem...
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