scholarly journals Pengenalan Logo Kendaraan Menggunakan Metode Local Binary Pattern dan Random Forest

2021 ◽  
Vol 5 (4) ◽  
pp. 639-646
Author(s):  
Alda Putri Utami ◽  
Febryanti Sthevanie ◽  
Kurniawan Nur Ramadhani
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Intelligent Transport System ◽  
Classification Problems ◽  
Feature Extraction Method ◽  
Logo Recognition ◽  
Image Dataset ◽  
Vehicle Logo Recognition

The vehicle logo is one of the features that can be used to identify a vehicle. Even so, a lot of Intelligent Transport System which are developed nowadays has yet to use a vehicle logo recognition system as one of its vehicle identification tools. Hence there are still cases of traffic crimes that haven't been able to be examined by the system, such as cases of counterfeiting vehicle license plates. Vehicle logo recognition itself could be done by using various feature extraction and classification methods. This research project uses the Local Binary Pattern feature extraction method which is often used for many kinds of image recognition systems. Then, the classification method used is Random Forest which is known to be effective and accurate for various classification problems. The data used for this study were as many as 2000 vehicle logo images consisting of 5 brand classes, namely Honda, Kia, Mazda, Mitsubishi, and Toyota. The results of the tests carried out obtained the best accuracy value of 88.89% for the front view logo image dataset, 77.03% for the side view logo image dataset, and 83% for the dataset with both types of images.

An Efficient Retinal Vessels Biometric Recognition System by Using Multi-Scale Local Binary Pattern Descriptor

2020 ◽  
Vol 10 (10) ◽  
pp. 2481-2489
Author(s):  
Muhammad Sheraz Arshad Malik ◽  
Qoseen Zahra ◽  
Imran Ullah Khan ◽  
Muhammad Awais ◽  
Gang Qiao
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Efficient Method ◽  
Large Scale ◽  
Local Binary Pattern ◽  
Evaluation Criteria ◽  
Recognition System ◽  
Suggested Approach ◽  
Multi Scale ◽  
Biometric Systems

Biometric systems are technically used for human recognition by identifying the unique features of an individual. Many security issues are found related to biometric systems such as voice, fingerprints, face, iris, signatures, etc., but the retina is a unique and efficient method to identify valid one. The aim of this paper is provided with an efficient method to recognize someone based on unique retina features. A proposed system based on retinal blood vessel pattern by using multi-scale local binary pattern (MSLBP) and random forest (Bagging tree) as feature extraction and classification. MSLBP is an efficient method to extracted features at six scales perpixel level, earlier work found the deficiency based on simple binary pattern with coverage of small areas and per-pixel level in the surrounding. MSLBP and random forest classifier suggested approach use for improving usability, perceivability, and sensitivity on large scale areas. It is the fastest method to get features accurately in an efficient way at every level of pixels. This method based on deep learning evaluation (criteria) parameter selection that provides more significant influence with sharp feature extraction on large scale areas based on seconds and improves the efficiency of images. MSLBP overcomes the problem of image sizing, pixel levels and efficiently provide accurate results.

scholarly journals A Multiscale Chaotic Feature Extraction Method for Speaker Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jiang Lin ◽  
Yi Yumei ◽  
Zhang Maosheng ◽  
Chen Defeng ◽  
Wang Chao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Extraction Method ◽  
State Of The Art ◽  
Recognition System ◽  
Nonlinear Dynamic Model ◽  
Feature Extraction Method ◽  
Analysis Technique ◽  
Recognition Systems ◽  
Environment Noise

In speaker recognition systems, feature extraction is a challenging task under environment noise conditions. To improve the robustness of the feature, we proposed a multiscale chaotic feature for speaker recognition. We use a multiresolution analysis technique to capture more finer information on different speakers in the frequency domain. Then, we extracted the speech chaotic characteristics based on the nonlinear dynamic model, which helps to improve the discrimination of features. Finally, we use a GMM-UBM model to develop a speaker recognition system. Our experimental results verified its good performance. Under clean speech and noise speech conditions, the ERR value of our method is reduced by 13.94% and 26.5% compared with the state-of-the-art method, respectively.

Wear Particles Recognition Based on Improved LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1874-1878
Author(s):  
Guo De Wang ◽  
Zhi Sheng Jing ◽  
Guo Wei Qin ◽  
Shan Chao Tu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Binary Sequence ◽  
Texture Feature ◽  
Experimental Results ◽  
Wear Particles ◽  
Texture Feature Extraction ◽  
Feature Extraction Method

Wear particles recognition is a key link in the process of Ferrography analysis. Different kinds of wear particles vary greatly in texture, texture feature is one of the most important feature in wear particles recognition. Local Binary Pattern (LBP) is an efficient operator for texture description. The binary sequence of traditional LBP operator is obtained by the comparison between the gray value of the neighborhood and the gray value of the center pixel of the neighborhood, the comparison is too simple to cause the loss of the texture. In this paper, an improved LBP operator is presented for texture feature extraction and it is applied to the recognition of severe sliding particles, fatigue spall particles and laminar particles. The experimental results show that our method is an effective feature extraction method and obtains better recognition accuracy compared with other methods.

scholarly journals FEATURE EXTRACTION BASED ON DIRECT CALCULATION OF MUTUAL INFORMATION

2007 ◽  
Vol 21 (07) ◽  
pp. 1213-1231 ◽  
Author(s):  
NOJUN KWAK
Keyword(s):  
Feature Extraction ◽  
Mutual Information ◽  
Direct Calculation ◽  
Extraction Methods ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Probability Density Estimation ◽  
Classification Problems ◽  
Feature Extraction Method ◽  
Window Method

In many pattern recognition problems, it is desirable to reduce the number of input features by extracting important features related to the problems. By focusing on only the problem-relevant features, the dimension of features can be greatly reduced and thereby can result in a better generalization performance with less computational complexity. In this paper, we propose a feature extraction method for handling classification problems. The proposed algorithm is used to search for a set of linear combinations of the original features, whose mutual information with the output class can be maximized. The mutual information between the extracted features and the output class is calculated by using the probability density estimation based on the Parzen window method. A greedy algorithm using the gradient descent method is used to determine the new features. The computational load is proportional to the square of the number of samples. The proposed method was applied to several classification problems, which showed better or comparable performances than the conventional feature extraction methods.

Research of Face Recognition Methods Based on Binding Feature Extraction

2014 ◽  
Vol 568-570 ◽  
pp. 668-671
Author(s):  
Yi Long ◽  
Fu Rong Liu ◽  
Guo Qing Qiu
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
Feature Extraction Method ◽  
Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

scholarly journals How to Utilize My App Reviews? A Novel Topics Extraction Machine Learning Schema for Strategic Business Purposes

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1310
Author(s):  
Ioannis Triantafyllou ◽  
Ioannis C. Drivas ◽  
Georgios Giannakopoulos
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Software Maintenance ◽  
Extraction Methods ◽  
Machine Learning Algorithms ◽  
Classification Problems ◽  
Case Scenario ◽  
Feature Extraction Method ◽  
Term Extraction ◽  
One Step

Acquiring knowledge about users’ opinion and what they say regarding specific features within an app, constitutes a solid steppingstone for understanding their needs and concerns. App review utilization helps project management teams to identify threads and opportunities for app software maintenance, optimization and strategic marketing purposes. Nevertheless, app user review classification for identifying valuable gems of information for app software improvement, is a complex and multidimensional issue. It requires foresight and multiple combinations of sophisticated text pre-processing, feature extraction and machine learning methods to efficiently classify app reviews into specific topics. Against this backdrop, we propose a novel feature engineering classification schema that is capable to identify more efficiently and earlier terms-words within reviews that could be classified into specific topics. For this reason, we present a novel feature extraction method, the DEVMAX.DF combined with different machine learning algorithms to propose a solution in app review classification problems. One step further, a simulation of a real case scenario takes place to validate the effectiveness of the proposed classification schema into different apps. After multiple experiments, results indicate that the proposed schema outperforms other term extraction methods such as TF.IDF and χ2 to classify app reviews into topics. To this end, the paper contributes to the knowledge expansion of research and practitioners with the purpose to reinforce their decision-making process within the realm of app reviews utilization.

An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

2019 ◽  
Vol 26 (3-4) ◽  
pp. 146-160
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yongbo Li ◽  
Xiaoqiang Du
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Extraction Method ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Multichannel Signals ◽  
Fault Feature Extraction ◽  
Fault Information

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

Pengenalan Finger Vein Menggunakan Local Line Binary Pattern dan Learning Vector Quantization

2018 ◽  
Vol 9 (2) ◽  
pp. 52-57
Author(s):  
Jayanti Yusmah Sari ◽  
Rizal Adi Saputra
Keyword(s):  
Feature Extraction ◽  
Vector Quantization ◽  
Recognition System ◽  
Learning Vector Quantization ◽  
Feature Extraction Method ◽  
Finger Vein ◽  
Straight Line ◽  
Vein Recognition ◽  
Form Features ◽  
Finger Vein Recognition

This research proposes finger vein recognition system using Local Line Binary Pattern (LLBP) method and Learning Vector Quantization (LVQ). LLBP is is the advanced feature extraction method of Local Binary Pattern (LBP) method that uses a combination of binary values from neighborhood pixels to form features of an image. The straight-line shape of LLBP can extract robust features from the images with unclear veins, it is more suitable to capture the pattern of vein in finger vein image. At the recognition stage, LVQ is used as a classification method to improve recognition accuracy, which has been shown in earlier studies to show better results than other classifier methods. The three main stages in this research are preprocessing, feature extraction using LLBP method and recognition using LVQ. The proposed methodology has been tested on the SDUMLA-HMT finger vein image database from Shandong University. The experiment shows that the proposed methodology can achieve accuracy up to 90%. Index Terms—finger vein recognition, Learning Vector Quantization, LLBP, Local Line Binary Pattern, LVQ.

Classification Method of EEG Based on Evolutionary Algorithm and Random Forest for Detection of Epilepsy

2020 ◽  
Vol 10 (5) ◽  
pp. 979-983
Author(s):  
Yan Fang ◽  
TaiSheng Zeng ◽  
Tianrong Song
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Detection System ◽  
Medical Profession ◽  
Difficult Problem ◽  
Neutral System ◽  
Analysis Method ◽  
Classification Problems ◽  
Brain Wave ◽  
Long Time

Epilepsy is a difficult problem that has puzzled the medical profession for a long time. The complexity, randomness, non-stationarity and nonlinearity of EEG signal of epilepsy bring great challenge to the detection of epilepsy. The study of epilepsy is an important subject of neutral system diseases. For automatic epilepsy detection system, the accuracy of identifying epilepsy and predicting epilepsy is of great significance to the treatment of doctors and the recovery of patients. This paper proposes the mixed feature extraction to extract the feature by mixture of time-domain method and nonlinear analysis method, and the extracted feature is optimized using evolutionary optimization algorithm, and finally train the epilepsy classifier by utilizing the optimized features through the Random forest algorithm. In the experiment, the accuracies of two-classification problems and three-classification problems respectively reach 99.2% and 98.1%. The results of cross-over experiment for many times show that, the method is of effectiveness in the classified feature extraction aiming at epilepsy brain wave.

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