Comparison of Methods for Batik Classification Using Multi Texton Histogram

Minarno, Agus Eko; Maulani, Ayu Septya; Kurniawardhani, Arrie; Bimantoro, Fitri; Suciati, Nanik

doi:10.12928/telkomnika.v16i3.7376

Cited by 16 publications

(10 citation statements)

References 20 publications

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“…These features will then be used as training data for machine learning algorithms during training or as inputs for models during prediction. Common feature extraction methods that are used for batik pattern recognition include GLCM [3,[6][7][8][9], scale-invariant feature transform (SIFT) [5,[10][11][12], multi texton histogram (MTH) [13], Gabor and log-Gabor [6], and local binary pattern (LBP) [6].…”

Section: Introductionmentioning

confidence: 99%

Batik pattern recognition using convolutional neural network

Rasyidi

Bariyah

2020

Bulletin EEI

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Batik is one of Indonesia's cultures that is well-known worldwide. Batik is a fabric that is painted using canting and liquid wax so that it forms patterns of high artistic value. In this study, we applied the convolutional neural network (CNN) to identify six batik patterns, namely Banji, Ceplok, Kawung, Mega Mendung, Parang, and Sekar Jagad. 994 images from the 6 categories were collected and then divided into training and test data with a ratio of 8:2. Image augmentation was also done to provide variations in training data as well as to prevent overfitting. Experimental results on the test data showed that CNN produced an excellent performance as indicated by accuracy of 94% and top-2 accuracy of 99% which was obtained using the DenseNet network architecture.

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Section: Introductionmentioning

confidence: 99%

Batik pattern recognition using convolutional neural network

Rasyidi

Bariyah

2020

Bulletin EEI

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“…Therefore, the NIR channel was used to refine the image's contrast. The HE method enhances the image by distributing the image brightness levels equally across the brightness scale [1], [11], [15], [21], [44], [53], [54], [72], [94], [119], [136], [140], [145], [150], [159], [168], [169], [172], [175]. Furthermore, the intensity of the contrast enhancement method is measured through the root mean square (RMS), where the higher the RMS value, the better the contrast image [22], [35], [48], [178]- [181].…”

Section: Analysis Of the Image Enhancement Methodsmentioning

confidence: 99%

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Zulkifle

Hassan

Ahmad

et al. 2021

IJEECS

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Recently, many researchers have directed their attention to methods of predicting shorelines by the use of multispectral images. Thus, a simple and optimised method using image enhancements is proposed to improve the low contrast of the Satellite pour l'Observation de la Terre-5 (SPOT-5) images in the detection of shorelines. The near-infrared (NIR) channel is important in this study to ensure the contrast of the vegetated area and sea classification, due to the high reflectance of leaves in the near infrared wavelength region. This study used five scenes of interest to show the different results in shoreline detection. The results demonstrated that the proposed method performed in an enhanced manner as compared to current methods when dealing with the low contrast ratio of SPOT-5 images. As a result, by utilising the near-infrared histogram equalization (NIR-HE), the contrast of all datasets was efficiently restored, producing a higher efficiency in edge detection, and achieving higher overall accuracy. The improved filtering method showed significantly better shoreline detection results than the other filter methods. It was concluded that this method would be useful for detecting and monitoring the shoreline edge in Tanjung Piai.

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“…Gray level co-occurrence matrix (GLCM) is defined as a matrix whose elements consist of pairs of pixels having a certain brightness level, where pairs of pixels are separated by distance d, with a θ angle [19][20][21]. GLCM is considered as the most common method based on the static approach for texture extraction and GLCM approach usually presented in a symmetrical matrix, increasing the required computational time [22].…”

Section: Gray Level Co-occurence Matrix (Glcm)mentioning

confidence: 99%

“…The total features utilized on the classifier are 16 features. In the experimental result, the authors compared the previous work of classification KNN and SVM using multi texton histogram [19]. The experimental result showed combination GLCM and SVM is better than previous work.…”

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confidence: 99%

Classification of batik patterns using K-Nearest neighbor and support vector machine

et al. 2020

Self Cite

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This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and Support Vector Machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include Energy, Entropy, Contras, Correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.

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Comparison of Methods for Batik Classification Using Multi Texton Histogram

Cited by 16 publications

References 20 publications

Batik pattern recognition using convolutional neural network

Batik pattern recognition using convolutional neural network

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Classification of batik patterns using K-Nearest neighbor and support vector machine

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