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The article is devoted to the construction of a mathematical model and the algorithm for reducing a set of staganalytical correlated methods taking into account the computational complexity of the problem. The basic steganographic methods for the images spatial domain are described. Known steganalytical algorithms for the spatial domain are reviewed. It is shown that a significant number of steganalytical...
This paper investigates feature selection method using filter Fast Correlation based Filter FCBF combined with Genetic Algorithm GA and particle swarm optimization PSO. In this paper two hybrid approaches based on filter method FCBF and Genetic algorithm (FCBF-GA) and filter FCBF with particle swarm (FCBF-PSO) are proposed. It has been found that the proposed method FCBF-PSO outperform the proposed...
Image Multi-label Classification (IMC) assigns a label or a set of labels to an image. The big demand for image annotation and archiving in the web attracts the researchers to develop many algorithms for this application domain. The Multi-Instance Multi-Label Learning (MIML) is an important type of machine learning framework proposed recently for IMC. In this framework, an image is described with...
In modern society, social networks play an important role for online users. However, one unignorable problem behind the booming of the services is privacy issues. This paper conducts an extensive study to infer sensitive personal information from public insensitive attributes in social networks by deploying different machine learning algorithms. The results show that several algorithms can achieve...
Automatic Target Recognition (ATR) aims at detecting the presence and at recognizing the typology and the orientation of targets within a scenario, by using an unsupervised approach. In Syntethic Aperture Radar imaging this turns to be a difficult task due to the specific characteristics of clutter and background noise. Within this manuscript a new two-steps ATR algorithm based on Kolmogorov-Smirnov...
Superpixel has been widely applied in hyperspectral image processing as a pre-processing step for over-segmentation. However, most superpixel algorithms are difficult to control the segmentation balance between fragmentation and accuracy. In this paper, we propose a superpixel aggregation model to cluster the over-segmentations. Based on the own importance and interrelationship of superpixels, a two-step...
The Euclid distance based K-means clustering is among the hard classification algorithms. When dealing with deterministic remote sensing data, it is difficult to gain satisfactory classification results using K-means algorithm. The traditional K-means clustering algorithm is faced with several shortcomings such as locally converged optimization, being sensitive to initial clustering centers, etc....
In this paper, a corpus creation of spontaneous facial expressions focused on learning usage is presented. This has been achieved through the recognition of EEG signals, the use of OpenCV library to detect facial expressions, and the execution of an image classification process in different categories, such as Boredom, Engagement, Interesting, Excitement, Focus, and Relax. Two different versions of...
Recommendation system becomes popular in recent years, because it provides more information about products which consumers are more likely to be interested in. However, bundling module, as one way of recommendations, does not appear on any product's page. In this paper, we will explore the impact of product characteristics on the bundling strategy implemented by recommendation system. We found that...
There are vigorous developments of social network which affect out life greatly. User influence is an important reason to promote the interaction in social network. When we analyze user influence, single value can’t indicate the user influence in different domains. This paper puts forward the design of User Classification PageRank (UCPR) to solve this problem. Firstly, we classify users according...
This article offers a method of structural representation of telemetry data, based on displaying the original data onto plane or torus. Such method allows potentially detect the implicit correlation dependences both in single data frame and in sequence of such frames. It offers algorithms of compression based on displaying the original data, represented as bit-form, onto the surface of plane or torus...
Traditional methods for hyperspectral image classification typically use raw spectral signatures without considering spatial characteristics. In this work, a classification algorithm based on Gabor features and decision fusion is proposed. First, the adjacent and high correlated spectral bands are intelligently grouped by coefficient correlation matrix. Following that, Gabor features in each group...
Apperceiving the road type timely is beneficial to the acceleration, braking and other operations of the unmanned vehicle, so as to improve the safety and reliability. Recognition of the road type plays an important role in the field of unmanned vehicles. In this paper, color correlation feature and Tamura texture feature has been extracted from the image taken by the car camera and fused together,...
In this paper, we present a number of statistically grounded performance evaluation metrics capable of evaluating binary classifiers in absence of annotated Ground Truth. These metrics are generic and can be applied to any type of classifier but are experimentally validated on binarization algorithms. We applied the statistically grounded metrics and compared them with metrics based on annotated data...
The change of the vibration signal can reflect the mechanical state of the HV circuit breaker. An efficient method of feature extraction of the vibration signal usually pays a key role in the validity of the fault diagnosis and also lays the foundation for the fault classification in the subsequent stage. The paper presented a feature extraction method which is based on average empirical mode decomposition...
This work proposes a texture classification algorithm using three elements of fractal analysis: fractal dimension, lacunarity and succolarity. Beside other papers, the interconnection of fractal elements is taken into account as a texture classification factor. The three fractal analysis elements are presented in the paper and were implemented using Matlab. Fractal dimension spectrum, mean of lacunarity...
Stereo matching is the key problem in many stereo vison based 3D applications. One of the factors make local stereo matching time-consuming is that every pixel has the same disparity range as the pre-set one, which should be larger than all possible disparities. An improper pre-set disparity range may lead to redundant computation for some pixels and inadequate computation for some others. In this...
The biggest concern of Network is security. Intro find the tricks and tools of the Attackers. Data Mining techniques automatically learn the pattern of the tuples and Intelligent decision are made. Supervised learning methods finds the attack based on previous knowledge and unknown attacks are detected by using Unsupervised learning. Dos, Probe and Normal data are correctly detected by maximum Data...
Image registration plays a major role in many areas such as remote sensing, astronomy, biomedical imaging, and so on. Our main contribution in this paper is to present a new subpixel image registration that aligns translated of pair images. This algorithm combines well-known phase correlation technique with the differential methods of the optical flow field, especially the Locus-Kanade technique to...
Buildings and adjacent objects in the high spatial resolution images Present the spatial correlation due to the spectral similarity. In addition, the spectral details of building top are completely reflected in images because of resolution levels increased from meter to sub-meter. K-means algorithm is a classical clustering algorithm. Fine spectrum, low signal to noise ratio (SNR) and high spatial...
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