scholarly journals Revealing Traces of Image Resampling and Resampling Antiforensics

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Anjie Peng ◽  
Yadong Wu ◽  
Xiangui Kang
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Ar Model ◽  
Support Vector ◽  
Difference Image ◽  
Art Works ◽  
Vector Machines ◽  
Image Tampering Detection ◽  
The Difference ◽  
High Pass

Image resampling is a common manipulation in image processing. The forensics of resampling plays an important role in image tampering detection, steganography, and steganalysis. In this paper, we proposed an effective and secure detector, which can simultaneously detect resampling and its forged resampling which is attacked by antiforensic schemes. We find that the interpolation operation used in the resampling and forged resampling makes these two kinds of image show different statistical behaviors from the unaltered images, especially in the high frequency domain. To reveal the traces left by the interpolation, we first apply multidirectional high-pass filters on an image and the residual to create multidirectional differences. Then, the difference is fit into an autoregressive (AR) model. Finally, the AR coefficients and normalized histograms of the difference are extracted as the feature. We assemble the feature extracted from each difference image to construct the comprehensive feature and feed it into support vector machines (SVM) to detect resampling and forged resampling. Experiments on a large image database show that the proposed detector is effective and secure. Compared with the state-of-the-art works, the proposed detector achieved significant improvements in the detection of downsampling or resampling under JPEG compression.

scholarly journals Contact Lens Classification by Using Segmented Lens Boundary Features

2018 ◽  
Vol 11 (3) ◽  
pp. 1129
Author(s):  
Nur Ariffin Mohd Zin ◽  
Hishammuddin Asmuni ◽  
Haza Nuzly Abdul Hamed ◽  
Razib M. Othman ◽  
Shahreen Kasim ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Contact Lens ◽  
State Of The Art ◽  
Classification Method ◽  
Support Vector ◽  
Local Descriptors ◽  
Iris Image ◽  
Vector Machines ◽  
False Reject Rate ◽  
Better Than

Recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art methods.

scholarly journals State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Ramin Keivani ◽  
Sina Faizollahzadeh Ardabili ◽  
Farshid Aram
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Smart Cities ◽  
Model Development ◽  
Urban Sustainability ◽  
Urban Transport ◽  
Support Vector ◽  
Neuro Fuzzy ◽  
Vector Machines

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

The impact of different parameter sets on the classification of asteroid types

2021 ◽  
Author(s):  
Hanna Klimczak ◽  
Wojciech Kotłowski ◽  
Dagmara Oszkiewicz ◽  
Francesca DeMeo ◽  
Agnieszka Kryszczyńska ◽  
...  
Keyword(s):  
Gradient Boosting ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Machine Learning Methods ◽  
Vector Machines ◽  
Science Centre ◽  
The Difference ◽  
The Impact

<p>The aim of the project is the classification of asteroids according to the most commonly used asteroid taxonomy (Bus-Demeo et al. 2009) with the use of various machine learning methods like Logistic Regression, Naive Bayes, Support Vector Machines, Gradient Boosting and Multilayer Perceptrons. Different parameter sets are used for classification in order to compare the quality of prediction with limited amount of data, namely the difference in performance between using the 0.45mu to 2.45mu spectral range and multiple spectral features, as well as performing the Prinicpal Component Analysis to reduce the dimensions of the spectral data.</p> <p> </p> <p>This work has been supported by grant No. 2017/25/B/ST9/00740 from the National Science Centre, Poland.</p>

scholarly journals Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Nalindren Naicker ◽  
Timothy Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Student Performance ◽  
State Of The Art ◽  
Learning Algorithms ◽  
The State ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Support Vector ◽  
Vector Machines

Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

scholarly journals K-mer based classifiers extract functionally relevant features to support accurate Peroxiredoxin subgroup distinction

2018 ◽  
Author(s):  
Jiajie Xiao ◽  
William H. Turkett
Keyword(s):  
Active Site ◽  
Antioxidant Defense ◽  
State Of The Art ◽  
Support Vector ◽  
Site Analysis ◽  
Representative Sequence ◽  
Current State ◽  
Vector Machines ◽  
Sequence Representation ◽  
Functional Relevance

AbstractBackgroundThe Peroxiredoxins (Prx) are a family of proteins that play a major role in antioxidant defense and peroxide-regulated signaling. Six distinct Prx subgroups have been defined based on analysis of structure and sequence regions in proximity to the Prx active site. Analysis of other sequence regions of these annotated proteins may improve the ability to distinguish subgroups and uncover additional representative sequence regions beyond the active site.ResultsThe space of Prx subgroup classifiers is surveyed to highlight similarities and differences in the available approaches. Exploiting the recent growth in annotated Prx proteins, a whole sequence-based classifier is presented that employs support vector machines and a k-mer (k=3) sequence representation.Distinguishing k-mers are extracted and located relative to published active site regions.ConclusionsThis work demonstrates that the 3-mer based classifier can attain high accuracy in subgroup annotation, at rates similar to the current state-of-the-art. Analysis of the classifier’s automatically derived models show that the classification decision is based on a combination of conserved features, including a significant number of residue regions that have not been previously suggested as informative by other classifiers but for which there is evidence of functional relevance.

scholarly journals Dynamic Features Based on Flow-Correlation and HOG for Recognition of Discrete Facial Expressions

2020 ◽  
Vol 34 (5) ◽  
pp. 585-594
Author(s):  
Shivangi Anthwal ◽  
Dinesh Ganotra
Keyword(s):  
Interpersonal Communication ◽  
Facial Expressions ◽  
Visual Information ◽  
Human Cognition ◽  
Support Vector ◽  
Dynamic Features ◽  
Difference Image ◽  
Neutral Face ◽  
The Difference ◽  
Emotional Face

Facial expressions are the most preeminent means of conveying one’s emotions and play a significant role in interpersonal communication. Researchers are in pursuit of endowing machines with the ability to interpret emotions from facial expressions as that will make human-computer interaction more efficient. With the objective of effective affect cognition from visual information, we present two dynamic descriptors that can recognise seven principal emotions. The variables of the appearance-based descriptor, FlowCorr, indicate intra-class similarity and inter-class difference by quantifying the degree of correlation of optical flow associated with the image pair and each pre-designed template describing the motion pattern associated with different expressions. The second shape-based descriptor, dyn-HOG, finds the HOG values of the difference image derived by subtracting neutral face from emotional face, and is demonstrated to be more discriminative than previously used static HOG descriptors for classifying facial expressions. Recognition accuracies with multi-class support vector machine obtained on the CK+ and KDEF-dyn datasets are competent with the results of state-of-the-art techniques and empirical analysis of human cognition of emotions.

scholarly journals Modelling Proteolytic Enzymes With Support Vector Machines

2011 ◽  
Vol 8 (3) ◽  
pp. 1-15
Author(s):  
Lionel Morgado ◽  
Carlos Pereira ◽  
Paula Veríssimo ◽  
António Dourado
Keyword(s):  
State Of The Art ◽  
Proteolytic Enzymes ◽  
Support Vector ◽  
Computational Techniques ◽  
Strong Activity ◽  
Discriminative Models ◽  
Vector Machines ◽  
Merops Database ◽  
Enzyme Detection ◽  
Silver Bullet

Summary The strong activity felt in proteomics during the last decade created huge amounts of data, for which the knowledge is limited. Retrieving information from these proteins is the next step. For that, computational techniques are indispensable. Although there is not yet a silver bullet approach to solve the problem of enzyme detection and classification, machine learning formulations such as the state-of-the-art Support Vector Machine (SVM) appear among the most reliable options. A SVM based framework for peptidase analysis, that recognizes the hierarchies demarked in the MEROPS database is presented. Feature selection with SVM-RFE is used to improve the discriminative models and build classifiers computationally more efficient than alignment based techniques.

scholarly journals A pairwise output coding method for multi-class EEG classification of a self-induced BCI

2018 ◽  
Vol 8 (2) ◽  
pp. 216-227
Author(s):  
Nurhan Gursel Ozmen ◽  
Levent Gumusel
Keyword(s):  
Classical Method ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Motor Tasks ◽  
Coding Method ◽  
Vector Machines ◽  
The Difference ◽  
Repetition Number ◽  
Output Coding

In brain computer interface (BCI) research, electroencephalography (EEG) is the most widely used method due to its noninvasiveness, high temporal resolution and portability. Most of the EEG-based BCI studies are aimed at developing methodologies for signal processing, feature extraction and classification. In this study, an experimental EEG study was carried out with six subjects performing imagery mental and motor tasks. We present a  multi-class EEG decoding with a novel pairwise output coding method of EEGs to improve the performance of self-induced BCI systems. This method involves an augmented one-versus-one multiclass classification with less time and reduced number of electrodes. Furthermore, a train repetition number is introduced in the training step to optimize the data selection. The difference among right and left hemispheres is also searched. Finally, the difference between experienced and novice subjects is also observed. The experimental results have demonstrated that, the use of proposed classification algorithm produces high classification accuracies (98%) with nine channels. Reduced numbers of channels (four channels) have 100% accuracies for mental tasks and 87% accuracies for motor tasks with Support Vector Machines (SVM). The classification accuracies are quite high though the proposed one-versus-one technique worked well compared to the classical method. The results would be promising for a real-time study.

A Novel Vision Based Inspector with Light

2012 ◽  
Vol 268-270 ◽  
pp. 1916-1921 ◽  
Author(s):  
Huan Jun Liu
Keyword(s):  
Support Vector Machines ◽  
Dynamic Analysis ◽  
Difference Method ◽  
Support Vector ◽  
Inspection System ◽  
Vector Machines ◽  
Rotating Liquid ◽  
Fusion Images ◽  
The Difference ◽  
Algorithm Base

This paper presents an novel vision based inspector with light to inspect the liquid in bottle. The inspector is designed to capture sequence images of the rotating liquid in the bottle. And the impurities in liquid are inspected by dynamic analysis. The difference method for fusion images is put forward to detect the motion regions in sequence images. In order to detect motion regions precisely, this paper employs a novel segmentation algorithm base on unsupervised learning. This algorithm combines the fuzzy C-means method with fuzzy support vector machines, and can subdivide the image into the impurities and background efficiently. The experiments demonstrate the inspection precision of the liquid inspection system is about 96.4%.

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