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In this paper a new vulnerability detecting method is proposed to detect buffer boundary violations. The main idea is to use the metric of array index manipulation rather than using any heuristic method. We employ a SVM-based classifier to classify the vulnerable functions and innocent functions. Then the vulnerable functions are fed to function call graph guided symbolic execution to precisely determine...
Spectral unmixing techniques decompose the pixels into constituent fractions in order to extract the subpixel information. This study reviews spectral unmixing techniques from a perspective different from earlier approaches in that the problem is studied from a classification as well as clustering perspective. In this research, we focus on addressing some core issues of spectral unmixing such as endmember...
Polarimetric synthetic aperture radar (PolSAR) is of great importance in the remote sensing, which can be used widely in both civil and military fields. However, existing classification methods cannot effectively utilize the spatial structure information of the SAR data. In this study, a classification method for PolSAR images based on non-negative tensor factorization (NTF) is proposed. The proposed...
Image classification is a fundamental problem in computer vision and pattern recognition. Feature extraction is often regarded as the key for classifying images. Traditional ways rely on handcrafted features heavily, such as SIFT and BoW. In this paper, we concentrate on recognizing some specific categories of images (e.g. adult content and political images) in Email. And most importantly we propose...
A fault diagnosis method is proposed for the mine hoist machine fault diversity and redundancy of fault data based on Rough Sets (RS) and Support Vector Machine (SVM). RS theory is used to analyze the stator current fault data of mine hoist machine in order to exclude uncertain, duplicate information. For getting the optimal decision table, the equivalence relationship of positive domains of between...
Class imbalance is a major problem in machine learning. It occurs when the number of instances in the majority class is significantly more than the number of instances in the minority class. This is a common problem which is recurring in most datasets, including the one used in this paper (i.e. direct marketing dataset). In direct marketing, businesses are interested in identifying potential buyers,...
A diamond adsorption detecting system based on machine learning is presented in this paper. The paper describes the system from the perspective of hardware and software design, and presents the image processing and machine learning algorithms applied in the system. The hardware includes three major parts — the camera, light source and support platform. The software includes modules of image acquisition,...
SVM classifiers with Half Against Half (HAH) architecture are reported to be the fastest classifier amongst other SVM classification architectures reported in literature. An attempt is made to enhance the speed of HAH SVM classifier and is named as Fast HAH (F-HAH) classifier. The performance of proposed F-HAH classifier is evaluated using speaker dependent and multi-speaker dependent isolated digits...
We describe a method for identifying and classifying acid-fast bacilli (AFB) and their associated morphotypes in the microscope-images of Ziehl-Neelsen stained sputum smears, in the context of tuberculosis (TB) screening by image processing. The importance of our work stems from the fact that the transformation of the classical rod-shaped AFB into certain other shapes is said to be related to TB drug-resistance...
This paper addresses the need to use the knowledge about the human perceived quality, adding machine learning models to the objective quality estimation. A new technique is proposed based on the division of images into several cells where the mean of the SSIM metric is computed. A sliding window over a grid of cells that divide the image will define a set of image descriptors that are aggregated using...
In this paper, we propose an Active Learning approach to query by example retrieval, using a retraining procedure that improves the understanding of the machine with respect to the human perception. The proposed method is based on Support Vector Machine (SVM) classifiers and requires a small number of training samples. The classifier is retrained several times in order to determine the optimal separating...
Automatic extraction of popular music ringtones have become an important and useful area for communications and telecommunications industry. Quick and batch extraction of music ringtones increases the convenience in practical application. In this paper, we propose an automatic technology to extract the ringtones from popular music based on the musical structural analysis. This is a meaning attempt...
In this paper we propose an automatic marine life monitoring system. First task in the monitoring process is to detect underwater moving objects as fishes. Second Task is to identify the species of the detected fish. Third task is to track the detected fish to avoid multiple counting and record their activities. Detection is performed using GMM based background subtraction method, classification is...
Imbalanced data is an inevitable problem in many real world problems, including bleeding detection from endoscopic videos with a fewer clinically significant examples outnumbered by normal examples. In this paper, we have presented a comprehensive analysis of six different classifier performance for different class distribution of training dataset. We have addressed two questions: 1. Is there any...
In this paper, a new technique for constructing feature vector from DCT coefficients for gender classification has been presented. Firstly, images are divided into 8 × 8 sub images. DCT coefficients are calculated for each block in image. New technique is used for constructing the feature vector from DCT coefficients. Finally, SVM with Rbf kernel is used for classifying the images into male and female...
This paper reviews the comparative performance of Support Vector Machine (SVM) using four different kernels, i.e., Linear, Polynomial, Radial Basis Function (RBF) and Sigmoid. Overall accuracy (OA), Kappa Index Analysis (KIA), Receiver Operating Characteristic (ROC) and Precision (P) have been considered as evaluation parameters in order to assess the predictive accuracy of SVM. Both high resolution...
This paper implements the face recognition system based on genetic algorithm. System is consisted of illumination compensation in video image, feature extraction, feature selection, classification, identification, and displaying. the recognition rate of human face recognition system with a good stability and strong practicality is above 90%. It has laid a good foundation for further study of face...
The novel method proposed in this article is a content classification method. Using techniques like Content Based Image Retrieval and Bag Of Words, we classified several scenes captured by a Kinect camera. The method is simple, robust and provides high accuracy according to the tests. We tested the method using various classifiers such as SVMs, decision trees, KNN. The results demonstrate that the...
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...
Real Call Detail Records (CDR) are analyzed and classified based on Support Vector Machine (SVM) algorithm. The daily classification results in three traffic classes. We use two different algorithms, K-means and SVM to check the classification efficiency. A second support vector regression (SVR) based algorithm is built to make an online prediction of traffic load using the history of CDRs. Then,...
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