scholarly journals PERCEIVING DIGITAL WATERMARK DETECTION AS IMAGE CLASSIFICATION PROBLEM

2016 ◽  
Vol 2 (1) ◽  
pp. 1-22
Author(s):  
P. Then ◽  
Y.C. Wang
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
False Negative ◽  
Digital Watermark ◽  
Classification Problem ◽  
Support Vector ◽  
Svm Classifier ◽  
Operating Characteristics ◽  
Negative Class ◽  
Correlation Detector

Digital watermark detection is treated as classification problem of image processing. For image classification that searches for a butterfly, an image can be classified as positive class that is a butterfly and negative class that is not a butterfly. Similarly, the watermarked and unwatermarked images are perceived as positive and negative class respectively. Hence, Support Vector Machine (SVM) is used as the classifier of watermarked and unwatermarked digital image due to its ability of separating both linearly and non-linearly separable data. Hyperplanes of various detectors are briefly elaborated to show how SVM's hyperplane is suitable for Stirmark attacked watermarked image. Cox’s spread spectrum watermarking scheme is used to embed the watermark into digital images. Then, Support Vector Machine is trained with both the watermarked and unwatermarked images. Training SVM eliminates the use of watermark during the detection process. Receiver Operating Characteristics (ROC) graphs are plotted to assess the false positive and false negative probability of both the correlation detector of the watermarking schemes and SVM classifier. Both watermarked and unwatermarked images are later attacked under Stirmark, and then tested on the correlation detector and SVM classifier. Remedies are suggested to preprocess the training data. The optimal setting of SVM parameters is also investigated and determined besides preprocessing. The preprocessing and optimal parameters setting enable the trained SVM to achieve substantially better results than those resulting from the correlation detector.

scholarly journals Categorized Image Classification using CNN Features with ECOC Framework

2019 ◽  
Vol 8 (2) ◽  
pp. 145-150
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Network Architecture ◽  
Classification Problem ◽  
Image Feature ◽  
Experimental Result ◽  
Support Vector ◽  
Svm Classifier ◽  
Multiclass Problem ◽  
Class Labels

Image Classification technique is used to classify images into categories. In this study, an application is presented to examine category based image classification by combining Support Vector Machine with error correcting output codes (ECOC) framework. The ResNet50 used as Network architecture, our image dataset include caltech101 images from 9 categories (classes) which builds our classification task a multiclass problem. ECOC is a commonly used framework to model multiclass classification problem. We present one-verses-all coding design of ECOC and apply to SVM classifier. A pre-trained CNN (convolution neural network) is used for extracting image feature and as a classifier Multiclass Support Vector Machine is used. The extracted features are then passed for classification via ECOC approach. The final classification result predicts the class labels. The application is implemented in Matlab using pre-trained CNN. The prediction accuracy of each category is evaluated and presented. The experimental result shows an accuracy of 97.6%. Further experiments are carried out on different dataset which showed that best accuracy is achieved using CNN with ECOC for multiclass problem.

Support Vector Machine Classifier with WHM Offset for Unbalanced Data

2008 ◽  
Vol 12 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Boyang Li ◽  
◽  
Jinglu Hu ◽  
Kotaro Hirasawa
Keyword(s):  
Support Vector Machine ◽  
Real World ◽  
Support Vector Machine Classifier ◽  
Classification Problem ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Real World Data ◽  
Unbalanced Classification ◽  
Improved Support Vector Machine

We propose an improved support vector machine (SVM) classifier by introducing a new offset, for solving the real-world unbalanced classification problem. The new offset is calculated based on the unbalanced support vectors resulting from the unbalanced training data. We developed a weighted harmonic mean (WHM) algorithm to further reduce the effects of noise on offset calculation. We apply the proposed approach to classify real-world data. Results of simulation demonstrate the effectiveness of our proposed approach.

ML2S-SVM: multi-label least-squares support vector machine classifiers

2019 ◽  
Vol 37 (6) ◽  
pp. 1040-1058 ◽  
Author(s):  
Shuo Xu ◽  
Xin An
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Image Classification ◽  
Classification Problem ◽  
Superior Performance ◽  
Support Vector ◽  
Data Sets ◽  
Classification Problems ◽  
Content Type ◽  
Classification Approach

Purpose Image classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance. Design/methodology/approach Many multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM (ML2S-SVM). Findings Experimental results on the emotions, scene, yeast and bibtex data sets indicate that the ML2S-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of ML2S-SVM, so it is necessary for users to identify proper parameters in advance. Originality/value On the basis of MTLS-SVM, a novel multi-label classification approach, ML2S-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification.

Organ-Based Medical Image Classification Using Support Vector Machine

2017 ◽  
Vol 8 (1) ◽  
pp. 18-30 ◽  
Author(s):  
Monali Y. Khachane
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Medical Image ◽  
Brain Mri ◽  
Texture Features ◽  
Classification Problem ◽  
Training Data ◽  
Support Vector ◽  
Medical Image Classification ◽  
Occurrence Matrix

Computer-Aided Detection/Diagnosis (CAD) through artificial Intelligence is emerging ara in Medical Image processing and health care to make the expert systems more and more intelligent. The aim of this paper is to analyze the performance of different feature extraction techniques for medical image classification problem. Efforts are made to classify Brain MRI and Knee MRI medical images. Gray Level Co-occurrence Matrix (GLCM) based texture features, DWT and DCT transform features and Invariant Moments are used to classify the data. Experimental results shown that the proposed system produced better results however the training data is less than testing data. Support Vector Machine classifier with linear kernel produced higher accuracy 100% when used with texture features.

scholarly journals Wearable Device-based Fall Detection System for Elderly Care Using Support Vector Machine (SVM) classifier

2018 ◽  
Vol 7 (4.36) ◽  
pp. 488
Author(s):  
Nur Syazarin Natasha Abd Aziz ◽  
Salwani Mohd Daud ◽  
Nurul Iman Mohd Sa’at
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Elderly Care ◽  
False Negative ◽  
False Negative Rate ◽  
Fall Detection ◽  
Wearable Device ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction And Selection

Fall is an increasing problem as people ageing. It may happen to anyone, but their incidence does increase with age. Hence, the elderly will be facing catastrophic consequences due to falls. Nevertheless, there are still vulnerable in its accuracy in categorizing and differentiating the Activities Daily Living (ADL) and falls as most of the existing systems cause false alarm. This paper presents the research and simulation of wearable device-based fall detection approach by addressing the building of wearable device-based fall detection system for elderly care by using mobile devices. Two main phases involve in this research: online phase and offline phase. Online phase covers in data acquisition step whereby the raw data of simulated fall by participants is collected via built-in-tri-axial accelerometer in a smartphone, then automatically sent towards the computer via wireless communication. Meanwhile, offline phase covers data pre-processing, feature extraction and selection and data classification where these steps are handled in offline mode. Support Vector Machine (SVM) classifier was employed, and evaluated in the analysis. Overall accuracy rate, sensitivity, specificity as well as False Positive Rate (FPR) and False Negative Rate (FNR) were calculated. The findings suggest that SVM with Polynomial (order 5) method which achieved 68.91% overall accuracy as well as producing only 24.46% FPR is the most precise model for fall detection system in this paper. This approach has the potential to be implemented and deploy in real mobile application in future.   

scholarly journals Support vector machine and deep-learning object detection for localisation of hard exudates

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Veronika Kurilová ◽  
Jozef Goga ◽  
Miloš Oravec ◽  
Jarmila Pavlovičová ◽  
Slavomír Kajan
Keyword(s):  
Support Vector Machine ◽  
False Positive ◽  
High Reliability ◽  
False Positive Rate ◽  
False Negative ◽  
Support Vector ◽  
Retinal Images ◽  
Svm Classifier ◽  
Hard Exudates ◽  
Positive Rate

AbstractHard exudates are one of the main clinical findings in the retinal images of patients with diabetic retinopathy. Detecting them early significantly impacts the treatment of underlying diseases; therefore, there is a need for automated systems with high reliability. We propose a novel method for identifying and localising hard exudates in retinal images. To achieve fast image pre-scanning, a support vector machine (SVM) classifier was combined with a faster region-based convolutional neural network (faster R-CNN) object detector for the localisation of exudates. Rapid pre-scanning filtered out exudate-free samples using a feature vector extracted from the pre-trained ResNet-50 network. Subsequently, the remaining samples were processed using a faster R-CNN detector for detailed analysis. When evaluating all the exudates as individual objects, the SVM classifier reduced the false positive rate by 29.7% and marginally increased the false negative rate by 16.2%. When evaluating all the images, we recorded a 50% reduction in the false positive rate, without any decrease in the number of false negatives. The interim results suggested that pre-scanning the samples using the SVM prior to implementing the deep-network object detector could simultaneously improve and speed up the current hard exudates detection method, especially when there is paucity of training data.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

scholarly journals A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
HungLinh Ao ◽  
Junsheng Cheng ◽  
Kenli Li ◽  
Tung Khac Truong
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Local Characteristic ◽  
Support Vector ◽  
Svm Classifier ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

scholarly journals Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jianwei Cui ◽  
Mengxiao Shan ◽  
Ruqiang Yan ◽  
Yahui Wu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Energy Difference ◽  
Test System ◽  
Support Vector ◽  
Svm Classifier ◽  
Aero Engine ◽  
Aero Engines ◽  
Optimal Set

This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT-) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

Shape-restricted support vector machine (SR-SVM): a SVM classifier taking supplementary shape information of input

2021 ◽  
Vol 40 (1) ◽  
pp. 1481-1494
Author(s):  
Geng Deng ◽  
Yaoguo Xie ◽  
Xindong Wang ◽  
Qiang Fu
Keyword(s):  
Support Vector Machine ◽  
Classification Performance ◽  
Research Literature ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Active Set ◽  
Shape Information ◽  
Convex Optimization Problem ◽  
Shape Restrictions

Many classification problems contain shape information from input features, such as monotonic, convex, and concave. In this research, we propose a new classifier, called Shape-Restricted Support Vector Machine (SR-SVM), which takes the component-wise shape information to enhance classification accuracy. There exists vast research literature on monotonic classification covering monotonic or ordinal shapes. Our proposed classifier extends to handle convex and concave types of features, and combinations of these types. While standard SVM uses linear separating hyperplanes, our novel SR-SVM essentially constructs non-parametric and nonlinear separating planes subject to component-wise shape restrictions. We formulate SR-SVM classifier as a convex optimization problem and solve it using an active-set algorithm. The approach applies basis function expansions on the input and effectively utilizes the standard SVM solver. We illustrate our methodology using simulation and real world examples, and show that SR-SVM improves the classification performance with additional shape information of input.

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