Video Steganalysis Exploiting Motion Vector Calibration-Based Features

2012 ◽  
Vol 482-484 ◽  
pp. 168-172 ◽  
Author(s):  
Yu Deng ◽  
Yun Jie Wu ◽  
Lin Na Zhou
Keyword(s):  
Support Vector Machine ◽  
Feature Vector ◽  
Motion Vector ◽  
Support Vector ◽  
Motion Vectors ◽  
Feature Vectors ◽  
Video Steganalysis

The motion vector (MV)-based steganography embeds the secret messages by modifying the motion vectors. So the traditional video steganalytic schemes cannot detect the presence of the hidden messages by MV-based steganography. In this paper, a novel calibration-based steganalytic scheme against MV-based steganography is presented. The features are derived from the shift differences between the original and calibrated MVs, and then the feature vector is constructed. Using the extracted feature vectors, the support vector machine (SVM) is trained to detect the presence of stego videos. Compared with other features, the proposed features have better performance even with the low embedding strength.

PROTEIN FOLD CLASSIFICATION WITH GENETIC ALGORITHMS AND FEATURE SELECTION

2009 ◽  
Vol 07 (05) ◽  
pp. 773-788 ◽  
Author(s):  
PENG CHEN ◽  
CHUNMEI LIU ◽  
LEGAND BURGE ◽  
MOHAMMAD MAHMOOD ◽  
WILLIAM SOUTHERLAND ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Training Dataset ◽  
Support Vector ◽  
Protein Fold ◽  
Classification Rate ◽  
Test Dataset ◽  
Feature Vectors ◽  
Protein Folds

Protein fold classification is a key step to predicting protein tertiary structures. This paper proposes a novel approach based on genetic algorithms and feature selection to classifying protein folds. Our dataset is divided into a training dataset and a test dataset. Each individual for the genetic algorithms represents a selection function of the feature vectors of the training dataset. A support vector machine is applied to each individual to evaluate the fitness value (fold classification rate) of each individual. The aim of the genetic algorithms is to search for the best individual that produces the highest fold classification rate. The best individual is then applied to the feature vectors of the test dataset and a support vector machine is built to classify protein folds based on selected features. Our experimental results on Ding and Dubchak's benchmark dataset of 27-class folds show that our approach achieves an accuracy of 71.28%, which outperforms current state-of-the-art protein fold predictors.

scholarly journals Sparse Representation and SVM Diagnosis Method Inter-Turn Short-Circuit Fault in PMSM

2019 ◽  
Vol 9 (2) ◽  
pp. 224 ◽  
Author(s):  
Siyuan Liang ◽  
Yong Chen ◽  
Hong Liang ◽  
Xu Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Sparse Representation ◽  
Permanent Magnet ◽  
Short Circuit ◽  
Small Sample ◽  
Support Vector ◽  
Feature Vectors ◽  
Diagnosis Method ◽  
Short Circuit Fault

Permanent magnet synchronous motors (PMSM) has the advantages of simple structure, small size, high efficiency, and high power factor, and a key dynamic source and is widely used in industry, equipment and electric vehicle. Aiming at its inter-turn short-circuit fault, this paper proposes a fault diagnosis method based on sparse representation and support vector machine (SVM). Firstly, the sparse representation is used to extract the first and second largest sparse coefficients of both current signal and vibration signals, and then they are composed into four-dimensional feature vectors. Secondly, the feature vectors are input into the support vector machine for fault diagnosis, which is suitable for small sample. Experiments on a permanent magnet synchronous motor with artificially set inter-turn short-circuit fault and a normal one showed that the method is feasible and accurate.

scholarly journals An IoT Based Smart Organic Drip Fertigation System for Eco-Friendly Agriculture using SVM Classifier and ThingSpeak

2020 ◽  
Vol 9 (4) ◽  
pp. 709-713
Keyword(s):  
Support Vector Machine ◽  
Soil Erosion ◽  
Support Vector ◽  
Svm Classifier ◽  
Chemical Fertilizers ◽  
Classification Result ◽  
Feature Vectors ◽  
Flow Feature ◽  
Classification Prediction ◽  
Drip Fertigation

a result, the proposed system helps in reducing soil erosion as only the required nutrients are injected via the drip system in order to reduce the usage of chemical fertilizers. In this paper, we use Support Vector Machine (SVM) to classify three (Temperature, Ph, Flow) feature vectors. The classification results will predict whether the obtained data is normal or abnormal and explore the accuracy of classification prediction by using SVM. Finally, the classification result obtained by applying SVM is uploaded to the ThingSpeak cloud.

Fault Diagnosis for Aircraft Engine Based on SVM Multiple Classifiers Fusion

2013 ◽  
Vol 433-435 ◽  
pp. 607-611
Author(s):  
Feng Tian ◽  
Wen Jie Li ◽  
Zhi Gang Feng ◽  
Rui Zhang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Recognition Rate ◽  
Aircraft Engine ◽  
Support Vector ◽  
Feature Vectors ◽  
Multiple Classifiers ◽  
The Neural Network ◽  
Multiple Classifier ◽  
Multiple Classifiers Fusion

Support vector machine (SVM) could well solve the over-learning and the low generalization ability of the neural network. But the single classifier cannot achieve satisfactory recognition rate and anti-interference ability. An aircraft engine fault diagnosis method based on support vector machine multiple classifiers is proposed in this paper. Firstly, sample characteristic information which constitutes the fault feature vectors obtained from the existing engine fault. Then, after training the SVM multiple classifier by faulty feature vectors, the SVM model of the fault diagnosis system is established; Finally, the trained SVM multiple classifier is used to recognize and classify the test faults. Applying the noise on the test samples, SVM multiple classifiers can still get a good diagnosis effect. It shows that the fault diagnosis algorithm has good robustness and can be applied to the study of aero engine fault diagnosis.

scholarly journals Rail Corrugation Detection and Characterization Using Computer Vision

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8335
Author(s):  
Harris Lee ◽  
Jiyoung Hong ◽  
Tariku W. Wendimagegn ◽  
Heekong Lee
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
High Performance ◽  
Feature Vector ◽  
Support Vector ◽  
Inspection Method ◽  
Rail Corrugation ◽  
Normal Surfaces ◽  
Art Works ◽  
Important Means

Rail corrugation appears as oscillatory wear on the rail surface caused by the interaction between the train wheels and the railway. Corrugation shortens railway service life and forces early rail replacement. Consequently, service can be suspended for days during rail replacement, adversely affecting an important means of transportation. We propose an inspection method for rail corrugation using computer vision through an algorithm based on feature descriptors to automatically distinguish corrugated from normal surfaces. We extract seven features and concatenate them to form a feature vector obtained from a railway image. The feature vector is then used to build support vector machine. Data were collected from seven different tracks as video streams acquired at 30 fps. The trained support vector machine was used to predict test frames of rails as being either corrugated or normal. The proposed method achieved a high performance, with 97.11% accuracy, 95.52% precision, and 97.97% recall. Experimental results show that our method is more effective in identifying corrugated images than reference state-of the art works.

Structural Health Monitoring of Truss Type Structures Using Statistical Approach

2014 ◽  
Author(s):  
Mahdi Saffari ◽  
Ramin Sedaghati ◽  
Ion Stiharu
Keyword(s):  
Support Vector Machine ◽  
Health Monitoring ◽  
Damage Index ◽  
Crack Size ◽  
Baseline Data ◽  
Support Vector ◽  
Feature Vectors ◽  
Space Truss ◽  
Damage Data ◽  
Space Truss Structure

This paper proposes an effective statistical based vibration health monitoring technique using Auto Regressive (AR) parameters and Support Vector Machine (SVM) for truss type structures. The finite element method has been utilized to obtain acceleration response signals of a space truss structure under random excitations. The signals are then processed to extract their AR parameters as the feature vectors in which the AR parameters of the healthy structure are considered to be the reference baseline data. A Damage Index is then defined to be the standard deviation of the feature vectors from the baseline data. The proposed index provides an effective tool to detect the damage in the structure. It is shown that using only one sensor, it is still possible to accurately detect the damage. To locate the damage, data classification technique based on Support Vector Machine (SVM) has been employed. It is shown that SVM can successfully classify different signals extracted from the structure. Finally extensive sensitivity analysis has been performed to study the effect of different parameter such as crack size, number of sensors and AR parameter numbers on the accuracy of detection and localization processes.

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