Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine

Ryfial Azhar; Desmin Tuwohingide; Dasrit Kamudi; Sarimuddin; Nanik Suciati

doi:10.1016/j.procs.2015.12.101

Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine

Ryfial Azhar, Desmin Tuwohingide, Dasrit Kamudi, Sarimuddin, Nanik Suciati

Research output: Contribution to journal › Conference article › peer-review

82 Citations (Scopus)

Abstract

Batik is a traditional fabric of Indonesian cultural heritage. Automatic batik image classification is required to preserve the wealth of traditional art of Indonesia. In such classification, a method to extract unique characteristics of batik image is important. Combination of Bag of Features (BOF) extracted using Scale-Invariant Feature Transform (SIFT) and Support Vector Machine (SVM) classifier which had been successfully implemented in various classification tasks such as hand gesture, natural images, vehicle images, is applied to batik image classification in this study. The experimental results show that average accuracy of this method reaches 97.67%, 95.47% and 79% in normal image, rotated image and scaled image, respectively.

Original language	English
Pages (from-to)	24-30
Number of pages	7
Journal	Procedia Computer Science
Volume	72
DOIs	https://doi.org/10.1016/j.procs.2015.12.101
Publication status	Published - 2015
Event	3rd Information Systems International Conference, 2015 - Shenzhen, China Duration: 16 Apr 2015 → 18 Apr 2015

Keywords

Bag of features
Batik Image Classification
Scale-Invariant Feature Transform
Support vector machine

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10.1016/j.procs.2015.12.101

Cite this

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title = "Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine",

abstract = "Batik is a traditional fabric of Indonesian cultural heritage. Automatic batik image classification is required to preserve the wealth of traditional art of Indonesia. In such classification, a method to extract unique characteristics of batik image is important. Combination of Bag of Features (BOF) extracted using Scale-Invariant Feature Transform (SIFT) and Support Vector Machine (SVM) classifier which had been successfully implemented in various classification tasks such as hand gesture, natural images, vehicle images, is applied to batik image classification in this study. The experimental results show that average accuracy of this method reaches 97.67%, 95.47% and 79% in normal image, rotated image and scaled image, respectively.",

keywords = "Bag of features, Batik Image Classification, Scale-Invariant Feature Transform, Support vector machine",

author = "Ryfial Azhar and Desmin Tuwohingide and Dasrit Kamudi and Sarimuddin and Nanik Suciati",

note = "Publisher Copyright: {\textcopyright} 2015 The Authors.; 3rd Information Systems International Conference, 2015 ; Conference date: 16-04-2015 Through 18-04-2015",

year = "2015",

doi = "10.1016/j.procs.2015.12.101",

language = "English",

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T1 - Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine

AU - Azhar, Ryfial

AU - Tuwohingide, Desmin

AU - Kamudi, Dasrit

AU - Sarimuddin,

AU - Suciati, Nanik

PY - 2015

Y1 - 2015

N2 - Batik is a traditional fabric of Indonesian cultural heritage. Automatic batik image classification is required to preserve the wealth of traditional art of Indonesia. In such classification, a method to extract unique characteristics of batik image is important. Combination of Bag of Features (BOF) extracted using Scale-Invariant Feature Transform (SIFT) and Support Vector Machine (SVM) classifier which had been successfully implemented in various classification tasks such as hand gesture, natural images, vehicle images, is applied to batik image classification in this study. The experimental results show that average accuracy of this method reaches 97.67%, 95.47% and 79% in normal image, rotated image and scaled image, respectively.

AB - Batik is a traditional fabric of Indonesian cultural heritage. Automatic batik image classification is required to preserve the wealth of traditional art of Indonesia. In such classification, a method to extract unique characteristics of batik image is important. Combination of Bag of Features (BOF) extracted using Scale-Invariant Feature Transform (SIFT) and Support Vector Machine (SVM) classifier which had been successfully implemented in various classification tasks such as hand gesture, natural images, vehicle images, is applied to batik image classification in this study. The experimental results show that average accuracy of this method reaches 97.67%, 95.47% and 79% in normal image, rotated image and scaled image, respectively.

KW - Bag of features

KW - Batik Image Classification

KW - Scale-Invariant Feature Transform

KW - Support vector machine

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DO - 10.1016/j.procs.2015.12.101

M3 - Conference article

AN - SCOPUS:84964005580

SN - 1877-0509

VL - 72

SP - 24

EP - 30

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 3rd Information Systems International Conference, 2015

Y2 - 16 April 2015 through 18 April 2015

ER -

Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine

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Keywords

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