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Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of...
By using Relevance Vector Machine (RVM) to solve the problem of sparse signal recovery, Bayesian Compressive Sensing (BCS) can obtain good performance in spectral discrete spike signal detection. However, in cognitive radio (CR) system, the spectrum of primary user's signal, which is continuous in narrowband and is block sparse in wideband, cannot be exactly recovered by BCS. In this paper, a Bayesian...
Detecting pedestrians is a challenging problem owing to the motion of the subjects, the camera and the background and to variations in pose, appearance, clothing, illumination and background clutter. The Region Covariance Matrix (RCM) descriptors show experimentally significantly out-performs existing feature sets for pedestrian detection. In this paper, we present an efficient features extraction...
In this paper, a method with a significantly reduced feature set is described which recognizes human actions from video sequences. The proposed method is based on the natural domain knowledge of the human figure such as proportions of the human body. The method is evaluated on two of the publicly available databases in the literature of activity recognition, the Weizmann and the UIUC databases. Results...
This paper mainly studies facial expression recognition with the components by face parsing (FP). Considering the disadvantage that different parts of face contain different amount of information for facial expression and the weighted function are not the same for different faces, an idea is proposed to recognize facial expression using components which are active in expression disclosure. The face...
The prevalent use of Online Social Networks (OSN) and the anonymity and lack of accountability they inherent from being online give rise to many problems related to finding the connection between the massive amount of text data on OSN and the people who actually wrote them. Analyzing text data for such purposes is called authorship analysis. This work is focused on one specific type of authorship...
In this paper, we propose a machine learning based spectrum sensing framework for a new cognitive radio (CR) scenario where the primary user (PU) operates under more than one transmit power level. Different from the existing algorithms where the primary transmit power levels are assumed to be known, the proposed approach does not require much prior knowledge of either the primary user or the environment...
In recent years, object tracking is often formulated as detection tasks. Although many methods of machine learning and statistical learning can effectively achieve discriminate target model, but they usually require a large amount of training samples for satisfactory precision. Combining advantages of ensemble learning and the support vector machine, this paper proposes a simple tracking method based...
Manual annotation of images is usually a mandatory task in many applications where no knowledge about the image is available. In presence of huge number of images, this task becomes very tedious and prone to human errors. In this paper, we contribute in automatic annotation of ancient manuscripts by discovering manuscript calligraphy. Ancient manuscripts count a very large number of Persian and Maghrebi...
Measurement of cognitive load using brain signalsis an important area of research in human behavior and psychology. Recently, there have been attempts to use low cost, commercially available Electroencephalogram (EEG) devices for the analysis of the cognitive load. Due to the reduced number of leads, these low resolution devices pose major challenges in signal processing as well as in feature extraction...
Malicious programs, also known as malware, often use code obfuscation techniques to make static analysis more difficult and to evade signature-based detection. To resolve this problem, various behavioral detection techniques have been proposed that focus on the run-time behaviors of programs in order to dynamically detect malicious ones. Most of these techniques describe the run-time behavior of a...
With the rapid advance in information technology, more and more information exchange platforms appear. People can freely exchange information on these platforms. However, not all information is reliable. To make correct decisions, it is necessary to detect and remove unreliable information. The main purpose of this study is to improve the reliability of hotel ranking by detecting and deleting outlier...
Physical inactivity is an important contributor to non-communicable diseases in countries of high income, and increasingly so in those of low and middle income. Physical inactivity is the leading cause of many diseases. It has been estimated that as many as 250,000 deaths per year in the United States, approximately 12% of the total, are attributable to a lack of regular physical activity. Measuring...
This work presents a novel seven-phase open-end winding induction motor drive supplied by the dual non-square matrix converter. The input to each of the matrix converter is three phase utility grid system and the output is seven phase voltages with variable voltage and frequency. The two matrix converters feeding a seven-phase induction motor are supplied from a common single three-phase utility source...
There are a number of methods for image recognition and they are mainly based on Bag-of-Words (BoW) model. These models can be divided into two categories: generative and discriminative models. Some of the generative models are Naïve Bayes, latent Dirichlet allocation and Probabilistic Latent Semantic Analysis. Discriminative methods are Nearest neighbor classification, Support Vector Machines and...
In this paper, we try to hybrid projection twin support vector machine (PTSVM) and Extreme Learning Machine(ELM). The experiments shows that ELM generally out performs SVM/LS-SVM in various kinds of cases. PTELM tries to use ELM to overcome the shortness of PTSVM, which lacks of flexibility to change nonlinear kernel mapping for complex samples distribution regions. In order to overcome the shortness...
Depth information from a captured scene can provide a more complete description of objects which can be exploited for recognition purposes. In this paper we propose a new approach to static hand gesture recognition that explores only depth information acquired with a Microsoft Kinect camera. Firstly, the regions of interest are extracted from depth data and a 3D point cloud is created taking into...
Multiple Instance Learning (MIL) has been an interesting topic in the machine learning community. Since proposed, it has been widely used in content-based image retrieval and classification. In the MIL setting, the samples are bags, which are made of instances. In positive bags, at least one instance is positive. Whereas negative bags have all negative instances. This makes it different from the supervised...
Over the past years, research on digital image forensics has become a hot topic in multimedia security. Among various forensics technologies, image resampling detection has become a standard detection tool in image forensics. Furthermore, examining parameters of geometric transformations such as scaling factors or rotation angles is very useful for exploring an image's overall processing history....
Fast growth of multimedia data (e.g. videos) on the web makes some challenges on regular searching methods. To this end, Content-Based Video Retrieval (CBVR) was introduced as a considerable research interest for managing the collected videos' search on the Internet. Furthermore, due to relating most of these videos to humans, human action retrieval is considered as a new topic in CBVR. In this paper,...
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