Pattern Recognition using Multiclass Support Vector Machine Method with Local Binary Pattern as Feature Extraction

Irsalinda, Nursyiva; Surono, Sugiyarto; Sary, Indah Dwi Ratna

doi:10.26554/sti.2022.7.3.269-274

Cited by 2 publications

(4 citation statements)

References 25 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Local Binary Pattern is an excellent method used for texture analysis which was first introduced by [7]. LBP uses 8 neighboring pixels and 1 central pixel which functions as a threshold value.…”

Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%

“…SVM is a learning system which uses parameters in the form of linear functions to predict or classify. SVM uses a feature space with high dimensions, and uses a learning algorithm based on the implementation optimization theory of statistical learning [7], [9], [22]. SVM is a classification method that is currently widely used and developed by many studies.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…This is because it is associated with many similar curves and circles from letter to letter. The difficulties that occur in learning Javanese script will affect the interest of young people in learning Javanese letters [6], [7]. In addition, there are not many applications regarding the introduction of Javanese letters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition

Sari,

Shelomita

et al. 2023

JAIS

View full text Add to dashboard Cite

Indonesia is a country that is rich in cultural diversity. An example of one such variety is the Javanese language. The letters that are usually used in Javanese are non-Latin letters or are usually known as Javanese script. However, along with advances in technology, the Javanese language is increasingly being forgotten. In the past, the Javanese script was used as a subject in schools, aiming for Indonesian students to continue to gain knowledge about the Javanese script. The initial step in the introduction of the Javanese script starts with the preprocessing process by changing the image of the Javanese script from the RGB image to a grayscale image which is then performed feature extraction, where the feature extraction used in this script recognition is texture extraction with the Local Binary Pattern (LBP) algorithm. The results of this processing are obtained information that can be used as a parameter in the Multi Support Vector Machine (SVM) classification to predict Javanese script images. In this study using the LBP method with the Multi SVM Algorithm as a classification algorithm produces a high accuracy of 90% in the recognition of Javanese script, better than using only Multi SVM with an accuracy of 80%.

show abstract

Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

See 1 more Smart Citation

The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition

Sari,

Shelomita

et al. 2023

JAIS

View full text Add to dashboard Cite

show abstract

“…The study conducted dimensional calculations using CBCT images and found that measuring the maxillary sinus bone can be utilized for gender identification [15]. Furthermore, a study employed Computed Tomography (CT) images to detect lung nodules, serving as indicators for various lung disorders.…”

Section: Introductionmentioning

confidence: 99%

Age Identification System with Panoramic Image Processing Digital Molar Dental Radiograph with Adaptive Region Growing Approach Method

Fauzi,

Tsani,

Oscandar

et al. 2023

JMECS

View full text Add to dashboard Cite

Forensics plays a crucial role in legal enforcement, particularly in cases where objects or human victims undergoing forensic identification have suffered significant damage. Teeth offer a robust solution in the identification process due to their resilience to various circumstances. Forensic odontology focuses on dental identification for judicial purposes. One crucial parameter in forensic odontology is age estimation. Generally, an individual's dental development is directly related to age, which can be observed through the dental pulp. The dental pulp tends to narrow or widen with increasing age. In this study, an image processing system using the Adaptive Region Growing Approach (ARGA) method was developed for dental pulp molar radiograph images. Subsequently, the dental pulp images were classified using the Support Vector Machine (SVM) method. The research process encompassed data collection, image processing, feature extraction, and molar dental pulp size classification. The results demonstrated an accuracy of over 80% in the system, using specific parameters such as an adjustment threshold of OTSU 1.15, a clip limit histogram Equalization of 0.1, a polynomial kernel type, and one against one coding type for data classification into four classes. This study concludes that the Adaptive Region Growing Approach (ARGA) method and Support Vector Machine (SVM) classification can be effectively implemented in age estimation using panoramic radiograph images. This has the potential for significant applications in forensic odontology, supporting victim identification in legal enforcement.

show abstract

Pattern Recognition using Multiclass Support Vector Machine Method with Local Binary Pattern as Feature Extraction

Cited by 2 publications

References 25 publications

The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition

The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition

Age Identification System with Panoramic Image Processing Digital Molar Dental Radiograph with Adaptive Region Growing Approach Method

Contact Info

Product

Resources

About