Feature selection and reduction for batik image retrieval

Hisyam Fahmi; Remmy A.M. Zen; Hadaiq R. Sanabila; Ida Nurhaida; Aniati Murni Arymurthy

doi:10.1145/3033288.3033327

Feature selection and reduction for batik image retrieval

Hisyam Fahmi, Remmy A.M. Zen, Hadaiq R. Sanabila, Ida Nurhaida, Aniati Murni Arymurthy

Faculty of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

Abstract

Batik is the fabric which is truly unique to Indonesia. Batik image retrieval is the research area which focuses on image processing and image retrieving based on its characteristics. This study investigated the performance of the feature selection and reduction on the batik retrieval process. The feature employed in this experiment is the combination of four feature extraction methods, which are Gabor filter, log-Gabor filter, GLCM, and LBP. SFFS methods is used to carry out the selection of features, meanwhile, PCA is used to perform the reduction feature. Based on the experiment, PCA can increase the precision about 17%. Meanwhile, SFFS can improve the execution time 1800 times faster.

Original language	English
Title of host publication	Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016
Publisher	Association for Computing Machinery
Pages	47-52
Number of pages	6
ISBN (Electronic)	9781450347938
DOIs	https://doi.org/10.1145/3033288.3033327
Publication status	Published - 17 Dec 2016
Event	5th International Conference on Network, Communication and Computing, ICNCC 2016 - Kyoto, Japan Duration: 17 Dec 2016 → 21 Dec 2016

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	5th International Conference on Network, Communication and Computing, ICNCC 2016
Country/Territory	Japan
City	Kyoto
Period	17/12/16 → 21/12/16

Keywords

Batik retrieval
Feature reduction
Feature selection
Principal Component Analysis
Sequential Forward Floating Selection

Access to Document

10.1145/3033288.3033327

Cite this

Fahmi, H., Zen, R. A. M., Sanabila, H. R., Nurhaida, I., & Arymurthy, A. M. (2016). Feature selection and reduction for batik image retrieval. In Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016 (pp. 47-52). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3033288.3033327

@inproceedings{1a422b0f2d3b4f90a542fc33016211a8,

title = "Feature selection and reduction for batik image retrieval",

abstract = "Batik is the fabric which is truly unique to Indonesia. Batik image retrieval is the research area which focuses on image processing and image retrieving based on its characteristics. This study investigated the performance of the feature selection and reduction on the batik retrieval process. The feature employed in this experiment is the combination of four feature extraction methods, which are Gabor filter, log-Gabor filter, GLCM, and LBP. SFFS methods is used to carry out the selection of features, meanwhile, PCA is used to perform the reduction feature. Based on the experiment, PCA can increase the precision about 17%. Meanwhile, SFFS can improve the execution time 1800 times faster.",

keywords = "Batik retrieval, Feature reduction, Feature selection, Principal Component Analysis, Sequential Forward Floating Selection",

author = "Hisyam Fahmi and Zen, {Remmy A.M.} and Sanabila, {Hadaiq R.} and Ida Nurhaida and Arymurthy, {Aniati Murni}",

note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 5th International Conference on Network, Communication and Computing, ICNCC 2016 ; Conference date: 17-12-2016 Through 21-12-2016",

year = "2016",

month = dec,

day = "17",

doi = "10.1145/3033288.3033327",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "47--52",

booktitle = "Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016",

address = "United States",

}

Fahmi, H, Zen, RAM, Sanabila, HR, Nurhaida, I & Arymurthy, AM 2016, Feature selection and reduction for batik image retrieval. in Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 47-52, 5th International Conference on Network, Communication and Computing, ICNCC 2016, Kyoto, Japan, 17/12/16. https://doi.org/10.1145/3033288.3033327

Feature selection and reduction for batik image retrieval. / Fahmi, Hisyam; Zen, Remmy A.M.; Sanabila, Hadaiq R. et al.
Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016. Association for Computing Machinery, 2016. p. 47-52 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Feature selection and reduction for batik image retrieval

AU - Fahmi, Hisyam

AU - Zen, Remmy A.M.

AU - Sanabila, Hadaiq R.

AU - Nurhaida, Ida

AU - Arymurthy, Aniati Murni

PY - 2016/12/17

Y1 - 2016/12/17

N2 - Batik is the fabric which is truly unique to Indonesia. Batik image retrieval is the research area which focuses on image processing and image retrieving based on its characteristics. This study investigated the performance of the feature selection and reduction on the batik retrieval process. The feature employed in this experiment is the combination of four feature extraction methods, which are Gabor filter, log-Gabor filter, GLCM, and LBP. SFFS methods is used to carry out the selection of features, meanwhile, PCA is used to perform the reduction feature. Based on the experiment, PCA can increase the precision about 17%. Meanwhile, SFFS can improve the execution time 1800 times faster.

AB - Batik is the fabric which is truly unique to Indonesia. Batik image retrieval is the research area which focuses on image processing and image retrieving based on its characteristics. This study investigated the performance of the feature selection and reduction on the batik retrieval process. The feature employed in this experiment is the combination of four feature extraction methods, which are Gabor filter, log-Gabor filter, GLCM, and LBP. SFFS methods is used to carry out the selection of features, meanwhile, PCA is used to perform the reduction feature. Based on the experiment, PCA can increase the precision about 17%. Meanwhile, SFFS can improve the execution time 1800 times faster.

KW - Batik retrieval

KW - Feature reduction

KW - Feature selection

KW - Principal Component Analysis

KW - Sequential Forward Floating Selection

UR - http://www.scopus.com/inward/record.url?scp=85018274554&partnerID=8YFLogxK

U2 - 10.1145/3033288.3033327

DO - 10.1145/3033288.3033327

M3 - Conference contribution

AN - SCOPUS:85018274554

T3 - ACM International Conference Proceeding Series

SP - 47

EP - 52

BT - Proceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016

PB - Association for Computing Machinery

T2 - 5th International Conference on Network, Communication and Computing, ICNCC 2016

Y2 - 17 December 2016 through 21 December 2016

ER -

Feature selection and reduction for batik image retrieval

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this