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Image classification using kernels have very great importance in remote sensing data. The goal of this work is to efficiently classify the large set of aerial images into different classes. This paper introduces a kernel based classification for aerial images. It uses Grand Unified Regularized Least Square (GURLS) and library for support vector machines (LIBSVM). This paper compares the performance...
Playback attack detection (PAD) is essentially a binary classification task which is used to identify the authentic recordings from the playback recordings. For PAD problem, the difference of the acoustic feature between the authentic and playback recordings mainly comes from the recording channel and the ambient noise. Motivated by the excellent performance of the Gaussian Mixture Model-Universal...
In this paper we proposed an improved novel approach to identify the person using iris recognition technique. This approach is based on Artificial Neural Network and Support Vector Machine (SVM) as an iris pattern classifier. Prior to classifier, region of interest i.e. iris region is segmented using Canny edge detector and Hough transform. Provided that the effect of eyelid and eyelashes get reduced...
In recent years, the detection of drowsiness based on Electroencephalogram (EEG) signal has been paid great attentions. Most of the popular algorithms used for Brain Computer Interface (BCI) applications are, the Support Vector Machine (SVM) and the Artificial Neuronal Network (ANN)). The challenge is to developed a drowsiness detection system that is at once adapt to an embedded implementation and...
Poses recognition is an important research topic because some situations require silent communication (sign language, surgeon poses to the nurse for assistance etc.). Traditionally, poses recognition requires high quality expensive cameras and complicated computer vision algorithms. This is not the case thanks to the Microsoft Kinect sensor which provides an inexpensive and easy way for real time...
In this paper, we present a comparative study between Daubechies-DCT approach, Discrete Cosine Transform (DCT) and Histograms of Oriented Gradient (HOG) under different kind of kernel function. We obtain Daubechies-DCT by fusing the DCT features and Daubechies features. The implementation of HOG achieved by dividing the face image into small connected regions, named cells, and for each cell compiling...
The increasing cardiac diseases of people in recent years demand an early detection of heart diseases using electrocardiogram (ECG) signal processing techniques. In this work we present a semi automatic scheme to discriminate patient-specific ECG beats by using a kernel based feature extraction technique called kernel canonical correlation analysis (KCCA). The heartbeat classification scheme uses...
The chemical burn is one of the major accidents and life treating process in the modern world. The proposed research attempts to find an automated solution for classifying chemical skin burn as superficial, partial thickness and full thickness.. The design and development of such a classifier is clinically very significant particularly, when it is used in remote areas and under emergencies. Towards...
HCI (Human Computer Interfacing) technology is now able to provide an alternative support to the speech disabled person who have undergone severe brain stroke or spinal cord injury. It has been presented here that amongst all bio potential signal Electro-Oculogram (EOG) signal has got the ability to represent all daily life characters which is most needed for communication. This paper is aimed to...
Non-stationary signal analysis based on visual stimulation has drawn extensive attention in BCI system to provide the promising services. The main task of this paper tries to evaluate specific pattern of each decimal number created in human brain using the specific features of EEG. For differentiating among the decimal numbers, salient features are extracted using time, frequency and time-frequency...
Sleep apnea contributes to a variety of health threatening problems. However, there is a extremely low public and medical awareness of this disease. In order to identify sleep apnea/hyopnea, some effective features have been extracted from ECG signal, PPG signal and EEG signal. In this work, a novel combined of features characterizing physiological signals for monitoring epochs of sleep apnea is presented...
Prediction of preterm labor is of great importance to reduce neonatal death. Analysis of electrohysterogram (EHG) could be considered as a proper tool for this aim. In this paper, the statistical and non-linear features have been extracted from EHG signals and then Support Vector machine (SVM) has been applied for classification between term and preterm labor. The dataset of this research consists...
Over the past decade, the field of automatic speaker recognition has been the subject of extensive research looking for an efficient determination of a person's identity. Despite the essential role played by acoustic characteristics in order to discriminate between speakers. The research of discriminative information about a person remains a major challenge. The main objective of this paper is to...
In this paper, we present, first, a new method for color feature extraction based on SURF detectors. Then, we proved its efficiency for flower image classification. Therefore, we described visual content of the flower images using compact and accurate descriptors. These features are combined and the learning process is performed using a multiple kernel framework with a SVM classifier. The proposed...
To relieve the curse of dimensionality in functional magnetic resonance imaging (fMRI), we combine analysis of variance (ANOVA) with a support vector machine (SVM) to form a feature-based classification method. ANOVA is applied to find a more compact representation of the data by extracting features from fMRI images. A linear kernel SVM classifier is then trained on the selected features. Combining...
Face recognition for biometric purposes has an advantage of being a non-contact process. Various face recognition algorithms has been proposed in the literature. The face recognition system mainly consists of two steps i.e. feature extraction / reduction and classification. One of the most popular tool, Principal Component Analysis (PCA) is used for feature extraction. For classification purpose,...
Nature language processing is an important part in data mining, which counts a lot in the internet age. Feature extraction effects the accuracy of text classification. This paper proposes a method of iterative feature space evolution to optimize the result. Adjusting the extended dictionary and the stop word list, we optimize the feature space time and again to get a better classifier model. The final...
In this paper, a novel unsupervised method for learning sparse features combined with support vector machines for classification is proposed. The classical SVM method has restrictions on the large-scale applications. This model uses sparse auto encoder, a deep learning algorithm, to improve the performance. Firstly, we use multiple layers of sparse auto encoder to learn the features of the data. Secondly,...
This paper is an exploration to find a way to get the person attributes in profiles. Considering those attributes exists in large volume of unstructured data, and it is very difficult to gain in a short time. So, we use a method combing the pattern and SVM to extract the person attributes. Firstly, we collect many raw profiles in websites by our configurable crawler. Secondly, we use statistic methods...
This paper addresses the problem of recognizing handwritten numerals for Gujarati Language. Three methods are presented for feature extraction. One belongs to the spatial domain and other two belongs to the transform domain. In first technique, a new method has been proposed for spatial domain which is based on Freeman chain code. This method obtains the global direction by considering n × n neighbourhood...
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