Feature Selection and Reduction for Batik Image Retrieval

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

doi:10.1145/3033288.3033327

Cited by 11 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dataset that we used in this study is batik image dataset that contained basic motif template form a certain class. The dataset contains 5 class; they are Ceplok, Kawung, Lereng, Parang, and Nitik [13]. In data preprocessing, the texture features have been extracted for every image; they are Gabor filters, log Gabor filters, GLCM, and LBP.…”

Section: Methodsmentioning

confidence: 99%

“…In 2015, Nurhaida et al introduced an approach to batik pattern recognition using SIFT as a feature extraction method [12]. Furthermore, Fahmi et al (in 2016), was conducted the Batik Image Retrieval using feature selection and reduction. This research shows that the selection and reduction feature process could improve the precision and reduce the execution time.…”

Section: Introductionmentioning

confidence: 99%

“…This research shows that the selection and reduction feature process could improve the precision and reduce the execution time. From the experiments shows that PCA feature reduction can improve the retrieval precision while SFFS can reduce the execution time [13]. In 2017, Nurhaida et al developed Texture Fusion for Batik Motif Retrieval System, they systematically investigates the impact of image texture features on batik motif retrieval performance [14].…”

Section: Introductionmentioning

confidence: 99%

“…In 2017, Nurhaida et al developed Texture Fusion for Batik Motif Retrieval System, they systematically investigates the impact of image texture features on batik motif retrieval performance [14]. Based on the result of earlier work [7] and [13], this study was focused in batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM and using PCA feature reduction to improve the classification accuracy and reduce the computational time.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, BN also acts as a regularizer and often eliminates the required of Dropout for regularisation [25]. This study proposed to used Deep Learning method, namely Deep Neural Network to make comparison with the previous study ( [7] and [13]) and used batch normalisation as regularisation in our model. DNN with Batch Normalization is expected to make the model more general so that it can improve the accuracy of the model.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition

Nurhaida

Ayumi

Fitrianah

et al. 2020

IJECE

Self Cite

View full text Add to dashboard Cite

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition

Nurhaida

Ayumi

Fitrianah

et al. 2020

IJECE

Self Cite

View full text Add to dashboard Cite

show abstract

Feature selection in image analysis: a survey

2019

View full text Add to dashboard Cite

Image analysis is a prolific field of research which has been broadly studied in the last decades, successfully applied to a great number of disciplines. Since the apparition of Big Data, the number of digital images is explosively growing, and a large amount of multimedia data is publicly available. Not only is it necessary to deal with this increasing number of images, but also to know which features extract from them, and feature selection can help in this scenario. The goal of this paper is to survey the most recent feature selection methods developed and/or applied to image analysis, covering the most popular fields such as image classification, image segmentation, etc. Finally, an experimental evaluation on several popular datasets using well-known feature selection methods is presented, bearing in mind that the aim is not to provide the best feature selection method, but to facilitate comparative studies for the research community.

show abstract