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This paper aims to develop a framework for vehicle type classification using convolutional neural network based on vehicle rear view images. Compared with the extraction of the appearance features from vehicle side view and frontal view images, there has been relatively little research on vehicle type classification by using vehicle rear view images' information. The vehicle rear view images are detected...
Matrix factorization is a popular low dimensional representation approach that plays an important role in many pattern recognition and computer vision domains. Among them, convex and semi-nonnegative matrix factorizations have attracted considerable interest, owing to its clustering interpretation. On the other hand, the generalized correlation function (correntropy) as the error measure does not...
The paper deals with the problem of stability during the solving of pattern recognition tasks from the point of view of transformation groups. It shows the possibility to avoid the necessity of regularization by using the geometric equaffine Lorentz transformation, exploiting as example the alpha-procedure.
Unlike Support Vector Machine (SVM), Kernel Minimum Classification Error (KMCE) training frees kernels from training samples and jointly optimizes weights and kernel locations. Focusing on this feature of KMCE training, we propose a new method for developing compact (small scale but highly accurate) kernel classifiers by applying KMCE training to support vectors (SVs) that are selected (based on the...
This paper presents the design of a convolutional neural network architecture using the MatConvNet library for MATLAB in order to achieve the recognition of 2 classes of hand gestures: ”open” and ”closed”. Six architectures were implemented to which their hyperparameters and depth were varied to observe their behavior through the validation error in the training and accuracy in the estimation of each...
This paper presents a general ConvNet architecture for video action recognition based on multiplicative interactions of spacetime features. Our model combines the appearance and motion pathways of a two-stream architecture by motion gating and is trained end-to-end. We theoretically motivate multiplicative gating functions for residual networks and empirically study their effect on classification...
Pattern recognition techniques have been widely used in security-sensitive applications to distinguish malicious samples from legitimate ones. However, there usually exist some intelligent attackers who intend to have malicious samples to be mis-classified as legitimate at test time, i.e. evasion attack. Current researches show that traditional Support Vector Machines (SVMs) are vulnerable to evasion...
Aiming at the deception phenomena in the electric power market that disorder the power trading, the early warning problem of power users' credit risk in the power market was studied. To solve the problem, an early warning model based on SVM (Support Vector Machine) was purposed. First the evaluation criteria system and grading standard were discussed in detail. Secondly the early warning model based...
Support vector machine (SVM) is a popular machine learning method and has been widely applied in many real-world applications. Since SVM is sensitive to noises, fuzzy SVM (FSVM) has been proposed to relieve the over-fitting problem caused by noises through assigning a fuzzy membership to each sample. Then, different samples make different contributions to the learning of classification hyperplane...
The paper proposes a classification model for human behavioral patterns recognition in which the decisions are provided based on several Support Vector Machines classifiers within a multi-level decision structure. SVMs are suitable for applications in which the input data feature spaces are very large, involving many features. The human behavior recognition is a relevant example of such application...
Nowadays, there are a lot of graph data in many fields such as biology, medicine, social networks and so on. However, it is difficult to detect anomaly and get the useful information if we want to apply the traditional algorithms in graph data. Statistical pattern recognition and structural pattern recognition are two main methods in pattern recognition. The disadvantage of statistical pattern recognition...
Infrared polarization imaging detection can be used to obtain not only the polarization state but also the radiation of target. With this method, the target that traditional photometry cannot detect can be settled. The degree and angle of polarization that used in polarization detection reflect different physical properties, and it is seriously redundancy along with intensity of images. A target detection...
Classification is one of the most researched issues in Machine Learning. In this study, the Lorentzian Support Vector Machine (LSVM) method is proposed that performs classification in Lorentzian space. This proposed new classifier forms a hyperplane separating the classes based on the Lorentzian metric and maximize margins between nearest points to the hyperplane according to the Lorentzian distance...
By passing of time, the size of data such as fMRI scans, speech signals and digital photographs becomes very high and it takes large amount of time for data processing. To overcome this problem, the dimensionality of data should be reduced. Whereas graph embedding introduces a successful framework for dimensionality reduction, we use it as the base of our proposed method. In this framework, similarity...
Recognizing and localizing a recurring pattern is a problem with a variety of applications such as classification and localization of home appliances from their activation signals and estimating the relative alignment between records of a natural repetitive electrocardiography (ECG) signals in Bio-medical data. Most common approaches for recognizing a recurring pattern are generative and focus on...
This paper presents VeinDeep, a system for using vein patterns to secure smartphones from opportunistic access, e.g. a device left unattended. VeinDeep takes advantage of infrared depth sensors, which at the time of writing have recently started to appear in smartphones for 3D indoor mapping and localisation. We find these sensors can be re-purposed to capture images of the unique vein patterns on...
Convolution neural network can gain optional solution by training dataset many times. But persons without experiments are very difficult to seek a good learning rate or a good convergence criterion. We propose a framework, which only are composed by many cheap computers, and by improved convolution network to handle this problem. In the framework, we use terminal server to dispatch initial parameter...
In this paper, we propose a novel entropic signature for graphs, where we probe the graphs by means of continuous-time quantum walks. More precisely, we characterise the structure of a graph through its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encapsulates the time-averaged behaviour of a continuous-time quantum walk on the graph, i.e., the ij-th element...
Although the design of low-level local spatiotemporal features has recently led to significant improvement of performance in many action recognition applications, much less attention has been given to the equally important problem how to organize such low-level features extracted from the videos into a higher-level representation suitable to represent and discriminate between many different action...
The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce...
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