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Neuroscience researchers have a keen interest in finding the connection between various brain regions of an organism. Researchers all across the globe are finding new connections everyday and it is very difficult to keep track of all those, so it is important to create a centralized system which is able to give the relation between brain entities. Databases like PubMed contains abstracts and references...
This paper has proposed gait recognition approach for analyzing and classifying human identification under carrying a bag and wearing a clothing thus improving recognition performances. The proposed method is based on detail wavelet features extracted from the Haar-wavelet decomposition of dynamic areas in the Gait Energy Image (GEI). Spectral Regression Kernel Discriminant Analysis (SRKDA) is then...
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...
This paper addresses the problem of automatic target recognition (ATR) using inverse synthetic aperture radar (ISAR) images. In this context, we propose a novel approach for feature extraction to describe precisely an aircraft target from ISAR images. In our approach, a visual attention model is adopted to separate the salient regions from the background. After that, the scale invariant feature transform...
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...
In this paper, classifying and indexing hierarchical video genres using Support Vector Machines (SVMs) are based on only audio features. In fact, segmentation parameters are extracted at block levels, which have a major benefit by capturing local temporal information. The main contribution of our study is to present a powerful combination between the two employed audio descriptors; Mel Frequency Cepstral...
This paper presents a novel method to recognize subtle emotions based on optical strain magnitude feature extraction from the temporal point of view. The common way that subtle emotions are exhibited by a person is in the form of visually observed micro-expressions, which usually occur only over a brief period of time. Optical strain allows small deformations on the face to be computed between successive...
Due to the simplicity and firm mathematical foundation, Support Vector Machines (SVMs) have been intensively used to solve classification problems. However, training SVMs on real world large-scale databases is computationally costly and sometimes infeasible when the dataset size is massive and non-stationary. In this paper, we propose an incremental learning approach that greatly reduces the time...
Accurate head pose estimation is significant for many applications such as face recognition and human-computer interaction. In this paper, we treat the head pose estimation as a classification problem and employ the Lie Algebrized Gaussians (LAG) feature as the representation approach for head image. The LAG feature, which is built on Gausssian Mixture Model (GMM), has the capability to preserve the...
Emotions are mental states that can be expressed by motion, speech and other physiological reactions. In human-to-human interaction emotion perception is the perception on the emotion of the other people, which, due to the nature of emotions is not so precise. On the other hand, perception on emotions in human-computer interaction is still an open problem. A lot of work is done in direction of finding...
Handwritten signatures are one of the most widely used biometrics, particularly in financial and legal transactions. Offline Signature verification is still one of the most challenging problems in biometrics. In this study, we have evaluated the performance of different classifiers for offline signature verification based upon local binary patterns feature set. The feature vector is formed by dividing...
On one hand, sparse coding, which is widely used in signal processing, consists of representing signals as linear combinations of few elementary patterns selected from a dedicated dictionary. The output is a sparse vector containing few coding coefficients and is called sparse code. On the other hand, Multilayer Perceptron (MLP) is a neural network classification method that learns non linear borders...
This paper introduces a binary large margin classifier that approximates each class with an hyper disk constructed from its training samples. For any pair of classes approximated with hyper disks, there is a corresponding linear separating hyper plane that maximizes the margin between them, and this can be found by solving a convex program that finds the closest pair of points on the hyper disks....
This paper aims at classifying changed from unchanged pattern in multi-acquisition data using kernel based support vector data description (SVDD). Indeed, SVDD is a well known method allowing to map the data into a high dimensional features space where an hypersphere encloses most patterns belonging to the ”un-changed” class. In this work, we propose a new kernel function which combines the characteristics...
This paper describes a new kernel wavelet-based anomaly detection technique for long-wave (LW) Forward Looking Infrared (FLIR) imagery. The proposed approach called kernel wavelet-RX algorithm is essentially an extension of the wavelet-RX algorithm (combination of wavelet transform and RX anomaly detector) to a high dimensional feature space (possibly infinite) via a certain nonlinear mapping function...
Besides cardiovascular diseases, heart attacks are the main cause of death around the world. Pre-monitoring or pre-diagnostic helps to prevent heart attacks and strokes. ECG plays a key role in this regard. In recent studies, SVM with different kernel functions and parameter values are applied for classification on ECG data. The classification model of SVM can be improved by assigning membership values...
We present a general, simple feature representation of sequences that allows efficient inexact matching, comparison and classification of sequential data. This approach, recently introduced for the problem of biological sequence classification, exploits a novel multi-scale representation of strings. The new representation leads to discovery of very efficient algorithms for string comparison, independent...
A new kernel-based learning algorithm called kernel affine subspace nearest point (KASNP) approach is proposed in this paper. Inspired by the geometrical explanation of support vector machines (SVMs) and its nearest point problem in convex hulls, we extend the convex hull of each class to its corresponding affine subspace in high dimensional space induced by kernel. In two class affine subspaces,...
Traditional support vector machine needs pre-assumed kernel functions. This paper proposes a method via gene expression programming to automatically construct the kernel. The contributions of this paper include: (1) proposing the concepts of GEP kernel and kernel tree; (2) proposing the properties of GEP kernel and the kernel relation theorem; (3) proposing GEP based support vector machine (KGEP-SVM),...
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