scholarly journals A High Performace of Local Binary Pattern on Classify Javanese Character Classification

2018 ◽  
Vol 5 (1) ◽  
pp. 8 ◽  
Author(s):  
Ajib Susanto ◽  
Daurat Sinaga ◽  
Christy Atika Sari ◽  
Eko Hari Rachmawanto ◽  
De Rosal Ignatius Moses Setiadi
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Local Binary Pattern ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Characteristic Extraction ◽  
Research Objects ◽  
Character Classification

The classification of Javanese character images is done with the aim of recognizing each character. The selected classification algorithm is K-Nearest Neighbor (KNN) at K = 1, 3, 5, 7, and 9. To improve KNN performance in Javanese character written by the author, and to prove that feature extraction is needed in the process image classification of Javanese character. In this study selected Local Binary Patter (LBP) as a feature extraction because there are research objects with a certain level of slope. The LBP parameters are used between [16 16], [32 32], [64 64], [128 128], and [256 256]. Experiments were performed on 80 training drawings and 40 test images. KNN values after combination with LBP characteristic extraction were 82.5% at K = 3 and LBP parameters [64 64].

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.

Automatic Classification of Wood Texture Using Local Binary Pattern & Fuzzy K-Nearest Neighbor

2014 ◽  
Vol 903 ◽  
pp. 315-320
Author(s):  
Ismail Mohd Khairuddin ◽  
Ali Abuassal ◽  
Ali Abdelrahim ◽  
Amar Faiz Zainal Abidin ◽  
Syahrul Hisham Mohamad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Local Binary Pattern ◽  
Nearest Neighbor ◽  
Automatic Classification ◽  
Previous Literature ◽  
K Nearest Neighbor

The price of the wood according to the type of wood. Classification of the woods can be done by studying its texture. This paper introduces Fuzzy k Nearest Neighbor to classify 25 types of wood. The woods images have been taken from the Wood Database of the Centre for Artificial Intelligence & Robotics, Universiti Teknologi Malaysia. The features of wood images are extracted using Local Binary Pattern. The results of this paper shows improvement in wood classification compare to the previous literature.

scholarly journals Image classification of malaria using hybrid algorithms: convolutional neural network and method to find appropriate K for K-nearest neighbor

2019 ◽  
Vol 16 (1) ◽  
pp. 382
Author(s):  
Wisit Lumchanow ◽  
Sakol Udomsiri
Keyword(s):  
Neural Network ◽  
Plasmodium Falciparum ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Plasmodium Vivax ◽  
Nearest Neighbor ◽  
Learning Rate ◽  
K Nearest Neighbor ◽  
Ring Form

<span>This paper presents image classification algorithms to improve the learning rate and to comparison the classification efficiency. Using convolutional neural network (CNN) for feature extraction and method to find appropriate k for k-nearest neighbor (KNN). Medical datasets were used in the experiments to classify <span>Plasmodium Vivax and Plasmodium Falciparum. Results of the study indicated that for Plasmodium Vivax in ring form, the appropriate k was 1 and the learning rate (LR) was 83.33%, Trophozoite (k=5, LR=91.67%),</span></span><span> Schizont (k=1, LR=83.33<span>%</span>), and Gametocyte (k=1, LR=<span lang="AR-SA" dir="RTL">91.67</span><span>%</span>) whereas </span><span>Plasmodium Falciparum in ring form</span><span> (k=7, LR=91.67%)<span>,</span> Trophozoite (k=1, LR=83.33%), Schizont (k=1, LR=91.67%) and Gametocyte (k=1, LR=100%).</span>

scholarly journals Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 111-119
Author(s):  
Daurat Sinaga ◽  
Feri Agustina ◽  
Noor Ageng Setiyanto ◽  
Suprayogi Suprayogi ◽  
Cahaya Jatmoko
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Correlation Energy ◽  
Training Data ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Testing Data ◽  
Occurrence Matrix

Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.

scholarly journals Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Shan Guan ◽  
Kai Zhao ◽  
Shuning Yang
Keyword(s):  
Feature Extraction ◽  
Decision Tree ◽  
Motor Imagery ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Classification Algorithm ◽  
Classification Error ◽  
K Nearest Neighbor ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm

This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective. For method 1, a subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM) is designed to identify MI tasks and reduce the classification error in the nonseparable region of FGMDRM. Method 2 includes a feature extraction algorithm and a classification algorithm. The feature extraction algorithm combines semisupervised joint mutual information (semi-JMI) with general discriminate analysis (GDA), namely, SJGDA, to reduce the dimension of vectors in the Riemannian tangent plane. And the classification algorithm replaces the FGMDRM in method 1 with k-nearest neighbor (KNN), named SSDT-KNN. By applying method 2 on BCI competition IV dataset 2a, the kappa value has been improved from 0.57 to 0.607 compared to the winner of dataset 2a. And method 2 also obtains high recognition rate on the other two datasets.

Land Cover/Land Use Classification of Urban Areas

1998 ◽  
Vol 12 (04) ◽  
pp. 475-489 ◽  
Author(s):  
Jukka Heikkonen ◽  
Aristide Varfis
Keyword(s):  
Land Use ◽  
Feature Extraction ◽  
Feature Selection ◽  
Land Cover ◽  
Urban Areas ◽  
Nearest Neighbor ◽  
Self Organizing Map ◽  
Land Use Classification ◽  
K Nearest Neighbor

This paper proposes a method for remote sensing based land cover/land use classification of urban areas. The method consists of the following four main stages: feature extraction, feature coding, feature selection and classification. In the feature extraction stage, statistical, textural and Gabor features are computed within local image windows of different sizes and orientations to provide a wide variety of potential features for the classification. Then the features are encoded and normalized by means of the Self-Organizing Map algorithm. For feature selection a CART (Classification and Regression Trees) based algorithm was developed to select a subset of features for each class within the classification scheme at hand. The selected subset of features is not attached to any specific classifier. Any classifier capable of representing possible skewed and multi-modal feature distributions can be employed, such as multi-layer perceptron (MLP) or k-nearest neighbor (k-NN). The paper reports experiments in land cover/land use classification with the Landsat TM and ERS-1 SAR images gathered over the city of Lisbon to show the potentials of the proposed method.

scholarly journals The Identification of Balinese Scripts’ Characters in Papyrus Based on Semantic Feature and K Nearest Neighbor

2016 ◽  
Vol 1 (3) ◽  
pp. 177
Author(s):  
Made Sudarma ◽  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Cultural Heritage ◽  
Cultural Values ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
Semantic Feature ◽  
K Nearest Neighbor ◽  
Nearest Neighborhood

Papyrus script is a cultural heritage in Bali. As we know, that the papyrus is a cultural matter which is rich in valuable cultural values. Issues or problems encountered today is that the papyrus are not well maintained. Thus, many papyrus becomes damaged because it is not stored properly. Papyrus script was written using Balinese script’s characters which having different features compared with Latin’s characters. Balinese script can be recognized with feature extraction owned by each Balinese script. KNN is a classification algorithm based on nearest neighborhood. KNN can be used to classify Balinese script’s features so that the test Balinese script’s features which having nearest neighborhood value with the trained Balinese script’s features will be recognized as the same Balinese script

scholarly journals Classification of Content based Medical Image Retrieval Using Texture and Shape feature with Neural Network

2017 ◽  
Vol 6 (4) ◽  
pp. 368
Author(s):  
Sweety Maniar ◽  
Jagdish S. Shah
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Medical Image ◽  
Nearest Neighbor ◽  
False Negative ◽  
Body Part ◽  
K Nearest Neighbor ◽  
True Negative ◽  
Class Prediction

Medical image classification and retrieval systems have been finding extensive use in the areas of image classification according to imaging modalities, body part and diseases. One of the major challenges in the medical classification is the large size images leading to a large number of extracted features which is a burden for the classification algorithm and the resources. In this paper, a novel approach for automatic classification of fundus images is proposed. The method uses image and data pre-processing techniques to improve the performance of machine learning classifiers.<em> </em>Some predominant image mining algorithms such as Classification, Regression Tree (CART), Neural Network, Naive Bayes (NB), Decision Tree (DT) K-Nearest Neighbor. The performance of MCBIR systems using texture and shape features efficient. . The possible outcomes of a two class prediction be represented as True positive (TP), True negative (TN), False Positive (FP) and False Negative (FN).

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