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

被引：40

作者：

Azhar, Ryfial ^{[1
]}

Tuwohingide, Desmin ^{[1
,2
]}

Kamudi, Dasrit ^{[1
,2
]}

Sarimuddin ^{[1
,3
]}

Suciati, Nanik ^{[1
]}

机构：

[1] Inst Teknol Sepuluh Nopember Surabaya, Dept Informat, Surabaya, Indonesia

[2] Politekn Negeri Nusa Utara Sulawesi Utara, Dept Informat Syst, Surabaya, Indonesia

[3] Univ Sembilanbelas November Kolaka, Dept Informat, Surabaya, Indonesia

来源：

THIRD INFORMATION SYSTEMS INTERNATIONAL CONFERENCE 2015 | 2015年 / 72卷

关键词：

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

D O I：

10.1016/j.procs.2015.12.101

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2015 The Authors. Published by Elsevier B.V.

引用

页码：24 / 30

页数：7

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