scholarly journals Classification of vehicles’ types using histogram oriented gradients: comparative study and modification

2020 ◽  
Vol 9 (4) ◽  
pp. 700
Author(s):  
Rania Salah El-Sayed ◽  
Mohamed Nour El-Sayed
Keyword(s):  
Comparative Study ◽  
Nearest Neighbor ◽  
Image Features ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Vehicle Type ◽  
Proposed Model ◽  
Laplacian Filter ◽  
The Comparative Study

This paper proposes an efficient model for recognizing and classifying a vehicle type. The model localizes each object in the image then identifies the vehicle type. The features of an image are extracted using the histogram oriented gradients (HOG) and ant colony optimization (ACO). A vehicle type is determined using different classifiers namely: the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and Softmax classifiers. The model is implemented and operated on two datasets of vehicles' images as test-beds. From the comparative study, the SVM outperforms the other adopted classifiers and is also better using HOG than that using ACO. A modification is done on HOG by adding the Laplacian filter to select the most significant image features. The accuracy of the SVM classifier using modified HOG outperforms that one using the traditional HOG. The proposed model is analyzed and discussed regardless the local geometric and photometric transformations like illumination variations.

scholarly journals Studi Komparasi Metode Machine Learning untuk Klasifikasi Citra Huruf Vokal Hiragana

2021 ◽  
Vol 5 (3) ◽  
pp. 905
Author(s):  
Muhammad Afrizal Amrustian ◽  
Vika Febri Muliati ◽  
Elsa Elvira Awal
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Image Classification ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Comparative Study

Japanese is one of the most difficult languages to understand and read. Japanese writing that does not use the alphabet is the reason for the difficulty of the Japanese language to read. There are three types of Japanese, namely kanji, katakana, and hiragana. Hiragana letters are the most commonly used type of writing. In addition, hiragana has a cursive nature, so each person's writing will be different. Machine learning methods can be used to read Japanese letters by recognizing the image of the letters. The Japanese letters that are used in this study are hiragana vowels. This study focuses on conducting a comparative study of machine learning methods for the image classification of Japanese letters. The machine learning methods that were successfully compared are Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbor. The results of the comparative study show that the K-Nearest Neighbor method is the best method for image classification of hiragana vowels. K-Nearest Neighbor gets an accuracy of 89.4% with a low error rate.

SUPPORT VECTOR MACHINE BASED AUTOMATIC CLASSIFICATION OF HUMAN BRAIN USING MR IMAGE FEATURES

2006 ◽  
Vol 06 (03) ◽  
pp. 357-370 ◽  
Author(s):  
HENRY SELVARAJ ◽  
S. THAMARAI SELVI ◽  
D. SELVATHI ◽  
R. RAMKUMAR
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Image Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Volume Data ◽  
K Nearest Neighbor ◽  
Incorrect Diagnosis ◽  
Quantitative Indication

This paper proposes an intelligent classification technique to identify two categories of MRI volume data as normal and abnormal. The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to incorrect diagnosis when a large number of MRIs are analyzed. In this work, the textural features are extracted from the MR data of patients and these features are used to classify a patient as belonging to normal (healthy brain) or abnormal (tumor brain). The categorization is obtained using various classifiers such as support vector machine (SVM), radial basis function, multilayer perceptron and k-nearest neighbor. The performance of these classifiers are analyzed and a quantitative indication of how better the SVM performance is when compared with other classifiers is presented. In intelligent computer aided health care system, the proposed classification system using SVM classifier can be used to assist the physician for accurate diagnosis.

scholarly journals PrismatoidPatNet54: An Accurate ECG Signal Classification Model Using Prismatoid Pattern-Based Learning Architecture

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1914
Author(s):  
Mehmet Ali Kobat ◽  
Ozkan Karaca ◽  
Prabal Datta Barua ◽  
Sengul Dogan
Keyword(s):  
Nearest Neighbor ◽  
Classification Model ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Ecg Signals ◽  
Statistical Feature ◽  
Proposed Model ◽  
Feature Extractor ◽  
Intelligent Assistant

Background and objective: Arrhythmia is a widely seen cardiologic ailment worldwide, and is diagnosed using electrocardiogram (ECG) signals. The ECG signals can be translated manually by human experts, but can also be scheduled to be carried out automatically by some agents. To easily diagnose arrhythmia, an intelligent assistant can be used. Machine learning-based automatic arrhythmia detection models have been proposed to create an intelligent assistant. Materials and Methods: In this work, we have used an ECG dataset. This dataset contains 1000 ECG signals with 17 categories. A new hand-modeled learning network is developed on this dataset, and this model uses a 3D shape (prismatoid) to create textural features. Moreover, a tunable Q wavelet transform with low oscillatory parameters and a statistical feature extractor has been applied to extract features at both low and high levels. The suggested prismatoid pattern and statistical feature extractor create features from 53 sub-bands. A neighborhood component analysis has been used to choose the most discriminative features. Two classifiers, k nearest neighbor (kNN) and support vector machine (SVM), were used to classify the selected top features with 10-fold cross-validation. Results: The calculated best accuracy rate of the proposed model is equal to 97.30% using the SVM classifier. Conclusion: The computed results clearly indicate the success of the proposed prismatoid pattern-based model.

Optimized Feature Subset Selection and Relevance Feedback for Image Retrieval Based on Multiresolution Enhanced Orthogonal Polynomials Model

2015 ◽  
Vol 6 (2) ◽  
pp. 25-40
Author(s):  
S. Sathiya Devi
Keyword(s):  
Image Retrieval ◽  
Orthogonal Polynomials ◽  
Relevance Feedback ◽  
Nearest Neighbor ◽  
Image Features ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Feature Subset ◽  
K Nearest Neighbor ◽  
Multi Objective Genetic Algorithm

In this paper, a simple image retrieval method incorporating relevance feedback based on the multiresolution enhanced orthogonal polynomials model is proposed. In the proposed method, the low level image features such as texture, shape and color are extracted from the reordered orthogonal polynomials model coefficients and linearly combined to form a multifeature set. Then the dimensionality of the multifeature set is reduced by utilizing multi objective Genetic Algorithm (GA) and multiclass binary Support Vector Machine (SVM). The obtained optimized multifeature set is used for image retrieval. In order to improve the retrieval accuracy and to bridge the semantic gap, a correlation based k-Nearest Neighbor (k-NN) method for relevance feedback is also proposed. In this method, an appropriate relevance score is computed for each image in the database based on relevant and non relevant set chosen by the user with correlation based k-NN method. The experiments are carried out with Corel and Caltech database images and the retrieval rates are computed. The proposed method with correlation based k-NN for relevance feedback gives an average retrieval rate of 94.67%.

BASIC HANDWRITTEN CHARACTER RECOGNITION FROM MULTI-LINGUAL IMAGE DATASET USING MULTI-RESOLUTION AND MULTI-DIRECTIONAL TRANSFORM

2012 ◽  
Vol 10 (05) ◽  
pp. 1250046 ◽  
Author(s):  
SHITALA PRASAD ◽  
GYANENDRA K. VERMA ◽  
BHUPESH KUMAR SINGH ◽  
PIYUSH KUMAR
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Morphological Alteration ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Novel Approach ◽  
Pros And Cons ◽  
Resolution Algorithm ◽  
Level Fusion

This paper, proposes a novel approach for feature extraction based on the segmentation and morphological alteration of handwritten multi-lingual characters. We explored multi-resolution and multi-directional transforms such as wavelet, curvelet and ridgelet transform to extract classifying features of handwritten multi-lingual images. Evaluating the pros and cons of each multi-resolution algorithm has been discussed and resolved that Curvelet-based features extraction is most promising for multi-lingual character recognition. We have also applied some morphological operation such as thinning and thickening then feature level fusion is performed in order to create robust feature vector for classification. The classification is performed with K-nearest neighbor (K-NN) and support vector machine (SVM) classifier with their relative performance. We experiment with our in-house dataset, compiled in our lab by more than 50 personnel.

scholarly journals Biometric authenticator algorithm based on multiresolution analysis

2020 ◽  
Vol 20 (3) ◽  
pp. 1332
Author(s):  
Soumia Kerrache ◽  
Beladgham Mohammed ◽  
Hamza Aymen ◽  
Kadri Ibrahim
Keyword(s):  
Feature Extraction ◽  
Multiresolution Analysis ◽  
Nearest Neighbor ◽  
Curvelet Transform ◽  
Principal Component ◽  
Image Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Fusion Approach

Features extraction is an essential process in identifying person biometrics because the effectiveness of the system depends on it. Multiresolution Analysis success can be used in the system of a person’s identification and pattern recognition. In this paper, we present a feature extraction method for two-dimensional face and iris authentication.  Our approach is a combination of principal component analysis (PCA) and curvelet transform as an improved fusion approach for feature extraction. The proposed fusion approach involves image denoising using 2D-Curvelet transform to achieve compact representations of curves singularities. This is followed by the application of PCA as a fusion rule to improve upon the spatial resolution. The limitations of the only PCA algorithm are a poor recognition speed and complex mathematical calculating load, to reduce these limitations, we are applying the curvelet transform. <br /> To assess the performance of the presented method, we have employed three classification techniques: Neural networks (NN), K-Nearest Neighbor (KNN) and Support Vector machines (SVM).<br />The results reveal that the extraction of image features is more efficient using Curvelet/PCA.

Snow Detection using In-Vehicle Video Camera with Texture-Based Image Features Utilizing K-Nearest Neighbor, Support Vector Machine, and Random Forest

2019 ◽  
Vol 2673 (8) ◽  
pp. 221-232 ◽  
Author(s):  
Md Nasim Khan ◽  
Mohamed M. Ahmed
Keyword(s):  
Real Time ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Detection System ◽  
Weather Conditions ◽  
Video Camera ◽  
Image Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Weather Information

Snowfall negatively affects pavement and visibility conditions, making it one of the major causes of motor vehicle crashes in winter weather. Therefore, providing drivers with real-time roadway weather information during adverse weather is crucial for safe driving. Although road weather stations can provide weather information, these stations are expensive and often do not represent real-time trajectory-level weather information. The main motivation of this study was to develop an affordable in-vehicle snow detection system which can provide trajectory-level weather information in real time. The system utilized SHRP2 Naturalistic Driving Study video data and was based on machine learning techniques. To train the snow detection models, two texture-based image features including gray level co-occurrence matrix (GLCM) and local binary pattern (LBP), and three classification algorithms: support vector machine (SVM), k-nearest neighbor (K-NN), and random forest (RF) were used. The analysis was done on an image dataset consisting of three weather conditions: clear, light snow, and heavy snow. While the highest overall prediction accuracy of the models based on the GLCM features was found to be around 86%, the models considering the LBP based features provided a much higher prediction accuracy of 96%. The snow detection system proposed in this study is cost effective, does not require a lot of technical support, and only needs a single video camera. With the advances in smartphone cameras, simple mobile apps with proper data connectivity can effectively be used to detect roadway weather conditions in real time with reasonable accuracy.

The Novel Indices of Short-Time Heart Rate Variability for Prediction of Cardiovascular and Cerebrovascular Events

2020 ◽  
Vol 10 (3) ◽  
pp. 769-774
Author(s):  
Shiliang Shao ◽  
Ting Wang ◽  
Chunhe Song ◽  
Yun Su ◽  
Xingchi Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nearest Neighbor ◽  
Singular Value ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Cerebrovascular Events ◽  
The Hilbert Transform ◽  
Short Time

In this paper, eight novel instantaneous indices of short-time heart rate variability (HRV) signals are proposed for prediction of cardiovascular and cerebrovascular events. The indices are based on Bubble Entropy (BE) and Singular Value Decompose (SVD). The process of indices calculation is as follows, firstly, the instantaneous amplitude (IA), instantaneous frequency (IF) and instantaneous phase (IP) of HRV signals are estimated by the Hilbert transform. Secondly, according to the HRV, IA, IP and IF, the BE and singular value (SV) is calculated, then eight novel indices are obtained, they are BEHRV, BEIA, BEIF, BEIP, SVHRV, SVIA, SVIF and SVIP. Last but not least, in order to evaluate the performance of the eight novel indices for prediction of cardiovascular and cerebrovascular events, the difference analysis of eight indices is carried out by t-test. According to the p value, seven of the eight indices BEHRV, BEIA, BEIF, BEIP, SVIA, SVIF and SVIP are thought to be the indices to discriminate the E group and N group. The K-nearest neighbor (KNN), support vector machine (SVM) and decision tree (DT) are applied on the seven novel indices. The results are that, seven novel indices are significantly different between the events and non-events groups, and the SVM classifier has the highest classification Acc and Spe for prediction of cardiovascular and cerebrovascular events, they are 88.31% and 90.19%, respectively.

Study on XML Retrieval Results Classification

2012 ◽  
Vol 263-266 ◽  
pp. 1773-1777
Author(s):  
Hong Yu ◽  
Xiao Lei Huang ◽  
Zhi Ling Wei ◽  
Chen Xia Yang
Keyword(s):  
Nearest Neighbor ◽  
Classification Performance ◽  
Feedback Mechanism ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Xml Retrieval ◽  
Text Documents ◽  
Plain Text ◽  
Xml Documents

Mining (classify or clustering) retrieval results to serve relevance feedback mechanism of search engine is an important solution to improve effectiveness of retrieval. Unlike plain text documents, since the XML documents are semi-structured data, for XML retrieval results classification, consider exploiting structure features of XML documents, such as tag paths and edges etc. We propose to use Support Vector Machine (SVM) classifier to classify XML retrieval results exploiting both their content and structure features. We implemented the classification method on XML retrieval results based on the IEEE SC corpus. Compared with k-nearest neighbor classification (KNN) on the same dataset in our application, SVM perform better. The experiment results have also shown that the use of structure features, especially tag paths and edges, can improve the classification performance significantly.

AUTOMATED DIAGNOSIS OF EPILEPSY USING CWT, HOS AND TEXTURE PARAMETERS

2013 ◽  
Vol 23 (03) ◽  
pp. 1350009 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
RATNA YANTI ◽  
JIA WEI ZHENG ◽  
M MUTHU RAMA KRISHNAN ◽  
JEN HONG TAN ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Texture Features ◽  
Epileptic Seizures ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Classification ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Brain Disorder

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

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