scholarly journals Virtual Sample Generation and Ensemble Learning Based Image Source Identification With Small Training Samples

2021 ◽  
Vol 13 (3) ◽  
pp. 34-46
Author(s):  
Shiqi Wu ◽  
Bo Wang ◽  
Jianxiang Zhao ◽  
Mengnan Zhao ◽  
Kun Zhong ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Source Identification ◽  
Diffusion Theory ◽  
Training Sample ◽  
Virtual Sample ◽  
Image Source ◽  
Camera Identification ◽  
Average Accuracy ◽  
Training Samples ◽  
Sample Set

Nowadays, source camera identification, which aims to identify the source camera of images, is quite important in the field of forensics. There is a problem that cannot be ignored that the existing methods are unreliable and even out of work in the case of the small training sample. To solve this problem, a virtual sample generation-based method is proposed in this paper, combined with the ensemble learning. In this paper, after constructing sub-sets of LBP features, the authors generate a virtual sample-based on the mega-trend-diffusion (MTD) method, which calculates the diffusion range of samples according to the trend diffusion theory, and then randomly generates virtual sample according to uniform distribution within this range. In the aspect of the classifier, an ensemble learning scheme is proposed to train multiple SVM-based classifiers to improve the accuracy of image source identification. The experimental results demonstrate that the proposed method achieves higher average accuracy than the state-of-the-art, which uses a small number of samples as the training sample set.

Feature Extraction Algorithm Based on Sample Set Reconstruction

2013 ◽  
Vol 347-350 ◽  
pp. 2241-2245
Author(s):  
Xiao Yuan Jing ◽  
Xiang Long Ge ◽  
Yong Fang Yao ◽  
Feng Nan Yu
Keyword(s):  
Feature Extraction ◽  
Image Recognition ◽  
Training Sample ◽  
Classification Performance ◽  
Related Information ◽  
Training Samples ◽  
Extraction Algorithm ◽  
The Difference ◽  
Sample Set ◽  
Traditional Image

When the number of labeled training samples is very small, the sample information people can use would be very little and the recognition rates of traditional image recognition methods are not satisfactory. However, there is often some related information contained in other databases that is helpful to feature extraction. Thus, it is considered to take full advantage of the data information in other databases by transfer learning. In this paper, the idea of transferring the samples is employed and further we propose a feature extraction approach based on sample set reconstruction. We realize the approach by reconstructing the training sample set using the difference information among the samples of other databases. Experimental results on three widely used face databases AR, FERET, CAS-PEAL are presented to demonstrate the efficacy of the proposed approach in classification performance.

EVALUATE DISSIMILARITY OF SAMPLES IN FEATURE SPACE FOR IMPROVING KPCA

2011 ◽  
Vol 10 (03) ◽  
pp. 479-495 ◽  
Author(s):  
XU YONG ◽  
DAVID ZHANG ◽  
JIAN YANG ◽  
JIN ZHONG ◽  
JINGYU YANG
Keyword(s):  
Feature Extraction ◽  
Linear Combination ◽  
Feature Space ◽  
Training Sample ◽  
Maximum Amount ◽  
Experimental Result ◽  
Feature Extraction Method ◽  
High Classification Accuracy ◽  
Training Samples ◽  
Sample Set

Since in the feature space the eigenvector is a linear combination of all the samples from the training sample set, the computational efficiency of KPCA-based feature extraction falls as the training sample set grows. In this paper, we propose a novel KPCA-based feature extraction method that assumes that an eigenvector can be expressed approximately as a linear combination of a subset of the training sample set ("nodes"). The new method selects maximally dissimilar samples as nodes. This allows the eigenvector to contain the maximum amount of information of the training sample set. By using the distance metric of training samples in the feature space to evaluate their dissimilarity, we devised a very simple and quite efficient algorithm to identify the nodes and to produce the sparse KPCA. The experimental result shows that the proposed method also obtains a high classification accuracy.

scholarly journals The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1613
Author(s):  
Man Li ◽  
Feng Li ◽  
Jiahui Pan ◽  
Dengyong Zhang ◽  
Suna Zhao ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
System Control ◽  
Average Accuracy ◽  
Training Samples ◽  
The Disabled ◽  
Limited Training Samples ◽  
The Stability ◽  
Electroencephalogram Eeg ◽  
Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

scholarly journals An Atmospheric Visibility Grading Method Based on Ensemble Learning and Stochastic Weight Average

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 869
Author(s):  
Xiuguo Zou ◽  
Jiahong Wu ◽  
Zhibin Cao ◽  
Yan Qian ◽  
Shixiu Zhang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Traffic Accidents ◽  
Feature Sets ◽  
Atmospheric Visibility ◽  
Average Accuracy ◽  
Level 3 ◽  
Visual Characteristics ◽  
Level 1 ◽  
Grading Model ◽  
Level 2

In order to adequately characterize the visual characteristics of atmospheric visibility and overcome the disadvantages of the traditional atmospheric visibility measurement method with significant dependence on preset reference objects, high cost, and complicated steps, this paper proposed an ensemble learning method for atmospheric visibility grading based on deep neural network and stochastic weight averaging. An experiment was conducted using the scene of an expressway, and three visibility levels were set, i.e., Level 1, Level 2, and Level 3. Firstly, the EfficientNet was transferred to extract the abstract features of the images. Then, training and grading were performed on the feature sets through the SoftMax regression model. Subsequently, the feature sets were ensembled using the method of stochastic weight averaging to obtain the atmospheric visibility grading model. The obtained datasets were input into the grading model and tested. The grading model classified the results into three categories, with the grading accuracy being 95.00%, 89.45%, and 90.91%, respectively, and the average accuracy of 91.79%. The results obtained by the proposed method were compared with those obtained by the existing methods, and the proposed method showed better performance than those of other methods. This method can be used to classify the atmospheric visibility of traffic and reduce the incidence of traffic accidents caused by atmospheric visibility.

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

Anti‐noise image source identification

2018 ◽  
Vol 31 (19) ◽  
Author(s):  
Yuying Liu ◽  
Yonggang Huang ◽  
Jiao Zhang ◽  
Hualei Shen
Keyword(s):  
Source Identification ◽  
Image Source ◽  
Noise Image

scholarly journals AIRBORNE HYPERSPECTRAL REMOTE SENSING FOR IDENTIFICATION GRASSLAND VEGETATION

2015 ◽  
Vol XL-3/W3 ◽  
pp. 427-431 ◽  
Author(s):  
P. Burai ◽  
T. Tomor ◽  
L. Bekő ◽  
B. Deák
Keyword(s):  
Image Classification ◽  
Learning Algorithm ◽  
Training Sample ◽  
Hyperspectral Data ◽  
Training Dataset ◽  
Classification Methods ◽  
Grassland Vegetation ◽  
Training Samples ◽  
Almost All ◽  
Noise Fraction

In our study we classified grassland vegetation types of an alkali landscape (Eastern Hungary), using different image classification methods for hyperspectral data. Our aim was to test the applicability of hyperspectral data in this complex system using various image classification methods. To reach the highest classification accuracy, we compared the performance of traditional image classifiers, machine learning algorithm, feature extraction (MNF-transformation) and various sizes of training dataset. Hyperspectral images were acquired by an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400–1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. We used twenty vegetation classes which were compiled based on the characteristic dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset using various training sample sizes between 10 and 30 pixels. In the case of the original bands, both SVM and RF classifiers provided high accuracy for almost all classes irrespectively of the number of the training pixels. We found that SVM and RF produced the best accuracy with the first nine MNF transformed bands. Our results suggest that in complex open landscapes, application of SVM can be a feasible solution, as this method provides higher accuracies compared to RF and MLC. SVM was not sensitive for the size of the training samples, which makes it an adequate tool for cases when the available number of training pixels are limited for some classes.

Research on the Installation and Alignment Method of Ship Multi-support Bearings Based on Different Confidence-Level Training Samples

2021 ◽  
pp. 1-9
Author(s):  
Yibin Deng ◽  
Xiaogang Yang ◽  
Shidong Fan ◽  
Hao Jin ◽  
Tao Su ◽  
...  
Keyword(s):  
Neural Network ◽  
Confidence Level ◽  
Rapid Development ◽  
Maximum Error ◽  
Training Sample ◽  
Measured Data ◽  
Engine Room ◽  
Training Samples ◽  
Force Coupling ◽  
Level Training

Because of the long propulsion shafting of special ships, the number of bearings is large and the number of measured bearing reaction data is small, which makes the installation of shafting difficult. To apply a small amount of measured data to the process of ship installation so as to accurately calculate the displacement value in the actual installation, this article proposes a method to calculate the displacement value of shafting intermediate bearing based on different confidence-level training samples. Taking a ro-ro ship as the research object, this research simulates the actual installation process, gives a higher confidence level to a small amount of measured data, constructs a new training sample set for machine learning, and finally obtains the genetic algorithm-backpropagation(GABP) neural network reflecting the actual installation process. At the same time, this research compares the accuracy between different confidence-level training sample shafting neural network and the shafting neural network without measured data, and the results show that the accuracy of shafting neural network with different confidence-level training samples is higher. Although as the adjustment times and the number of measured data increase, the network accuracy is significantly improved. After adding four measured data, the maximum error is within 1%, which can play a guiding role in the ship propulsion shafting alignment. Introduction With the rapid development of science and technology in the world, special ships such as engineering ships, official ships, and warships play an important role (Carrasco et al. 2020; Prill et al. 2020). Some ships of this special type are limited by various factors such as the stern line of engine room, hull stability, and operation requirements. They usually adopt the layout of middle or front engine room, which causes the propulsion system to have a longer shaft and the number of intermediate shafts and intermediate bearings exceeds two. This forms a so-called multisupport shafting (Lee et al. 2019) and it increases the difficulty of shafting alignment because of the force-coupling between the bearings (Lai et al. 2018a, 2018b). The process of the existing methods for calculating the displacement value is complex, and because of the influence of installation error and other factors, it is necessary to adjust the bearing height several times to make the bearing reaction meet the specification requirements(Kim et al. 2017, Ko et al. 2017). So how to predict the accurate displacement value of each intermediate bearing is the key to solving the problem of multisupport shafting intermediate bearing installation and calibration (Zhou et al. 2005, Xiao-fei et al. 2017).

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