Batik classification using neural network with gray level co-occurence matrix and statistical color feature extraction

Aditya, Christian Sri Kusuma; Hani’ah, Mamluatul; Bintana, Rizqa Raaiqa; Suciati, Nanik

doi:10.1109/icts.2015.7379892

Cited by 23 publications

(9 citation statements)

References 8 publications

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“…There have been some studies on pattern recognition and classification of traditional cloth motif, but they mostly conducted on Batik. The most common feature extraction method used for Batik motif is Gray Level Co-Occurrence Matrices (GLCM) [3]- [6]. Based on studies that compare the performance of feature extraction methods, it is shown that GLCM bested Canny Edge Detection and Gabor Filters on classifying Batik motifs.…”

Section: Introductionmentioning

confidence: 99%

“…Suciati [10] used the Backpropagation Neural Network (BPNN) with Color-Texture-Based Feature Extraction to classify seven groups of Batik motifs with the rate of 0.37 Tanimoto Distance. Aditya et al [11], also used BPNN in combination with GLCM and comes with some promising results of more than 90% precision rate. Meanwhile, Azhar et al [12] used SIFT with Support Vector Machine (SVM) classifier on Batik motifs.…”

Section: Introductionmentioning

confidence: 99%

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Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)

et al. 2019

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Sasirangan is one of the traditional cloth from Indonesia. Specifically, it comes from South Borneo. It has many variations of motifs with a different meaning for each pattern. This paper proposes a prototype of Sasirangan motifs classification using four (4) type of Sasirangan motifs namely Hiris Gagatas, Gigi Haruan, Kulat Kurikit, and Hiris Pudak. We used primary data of Sasirangan images collected from Kampung Sasirangan, Banjarmasin, South Kalimantan. After that, the images are processed using Scale-Invariant Feature Transform (SIFT) to extract its features. Furthermore, the extracted features vectors obtained is classified using the Support Vector Machine (SVM). The result shows that the Scale- Invariant Feature Transform (SIFT) feature extraction with Support Vector Machine (SVM) classification able to classify Sasirangan motifs with an overall accuracy of 95%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)

et al. 2019

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“…Indonesian batik motifs have distinctive features according to their respective regions, for example Jogja batik, Solo batik, Lasem batik, and Pekalongan batik. This difference in motives is influenced by differences in geographical location, customs, arts and culture [2]. One of the famous batik that has a high degree of complexity, namely batik written lasem, no wonder if many hunted by middle and upper class batik collectors.…”

Section: Introductionmentioning

confidence: 99%

A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor

Jatmoko

Sinaga

2019

JAIS

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In this study, batik has been modeled using the GLCM method which will produce features of energy, contrast, correlation, homogenity and entropy. Then these features are used as input for the classification process of training data and data testing using the K-NN method by using ecludean distance search. The next classification uses 5 features that provide information on energy values, contrast, correlation, homogeneity, and entropy. Of the two classifications, which comparison will produce the best accuracy. Training data and data testing were tested using the Recognition Rate calculation for system evaluation. The results of the study produced 66% recognition rate in 50 pieces of test data and 100 pieces of training data.

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“…The accuracy obtained is by 80%, able to recognize batik image based on texture features. In addition to the use of texture features, batik image can also be classified by combin ing the features of texture and color statistics [12]. The texture features are extracted fro m the gray-level co-occurrence matrices (GLCM) consisting of contrast, correlation, energy, and homogeneity.…”

Section: Introductionmentioning

confidence: 99%

Batik Classification with Artificial Neural Network Based on Texture-Shape Feature of Main Ornament

Kasim¹,

Wardoyo²,

Harjoko³

2017

IJISA

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Abstract-Batik is a text ile with mot ifs of Indonesian culture wh ich has been recognized by UNESCO as world cultural heritage. Bat ik has many motifs which are classified in various classes of batik. This study aims to combine the features of texture and the feature of shapes' ornament in batik to classify images using artificial neural networks. The value of texture features of images in batik is extracted using a gray level co-occurrence matrices (GLCM) wh ich include Angular Second Moment (ASM) / energy), contrast, correlation, and inverse different mo ment (IDM ). The value of shape features is extracted using a binary mo rphological operation which includes compactness, eccentricity, rectangularity and solidity. At this phase of the training and testing, we compare the value of a classification accuracy of neural networks in each class in batik with their texture features, their shape, and the combination of texture and shape features. From the three features used in the classification of batik image with artificial neural networks, it was obtained that shape feature has the lowest accuracy rate of 80.95% and the combination of texture and shape features produces a greater value of accuracy by 90.48%. The results obtained in this study indicate that there is an increase in accuracy of batik image classification using the artificial neural network with the co mb ination of texture and shape features in batik image.

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Batik classification using neural network with gray level co-occurence matrix and statistical color feature extraction

Cited by 23 publications

References 8 publications

Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)

Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)

A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor

Batik Classification with Artificial Neural Network Based on Texture-Shape Feature of Main Ornament

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