Batik Classification using Microstructure Co-occurrence Histogram

Agus Minarno - Universitas Gadjah Mada, Jl, Grafika 2 Yogyakarta, 55281, Indonesia
Indah Soesanti - Universitas Gadjah Mada, Jl, Grafika 2 Yogyakarta, 55281, Indonesia
Hanung Nugroho - Universitas Gadjah Mada, Jl, Grafika 2 Yogyakarta, 55281, Indonesia

Citation Format:



Batik Nitik is a distinctive form of batik originating from the culturally rich region of Yogyakarta, Indonesia. What sets it apart from other batik styles is its remarkable motif similarity, a characteristic that often poses a considerable challenge when attempting to distinguish one design from another. To address this challenge, extensive research has been conducted with the primary objective of classifying Batik Nitik, and this research leverages an innovative approach combining the microstructure histogram and gray level co-occurrence matrix (GLCM) techniques, collectively referred to as the Microstructure Co-occurrence Histogram (MCH).The MCH method offers a multi-faceted approach to feature extraction, simultaneously capturing color, texture, and shape attributes, thereby generating a set of local features that faithfully represent the intricate details found in Batik Nitik imagery. In parallel, the GLCM method excels at extracting robust texture features by employing statistical measures to portray the subtle nuances within these batik patterns. Nevertheless, the mere fusion of microstructure and GLCM features doesn't inherently guarantee superior classification performance. This research paper has meticulously examined many feature fusion scenarios between microstructure and GLCM to pinpoint the optimal configuration that would yield the most accurate results. The dataset used consists of 960 Batik Nitik samples, comprising 60 categories. The classifiers employed in this study are K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), and Linear Discriminant Analysis (LDA). Based on the experimental results, the fusion of microstructure and GLCM features with the (LDA) classifier yields the best performance compared to other scenarios and classifiers.


Batik Nitik, classification; gray level co-occurrence matrix; feature fusion; Microstructure Co-occurrence Histogram

Full Text:



M. Garg and G. Dhiman, “A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants,” Neural Comput Appl, vol. 33, no. 4, pp. 1311–1328, Feb. 2021, doi: 10.1007/S00521-020-05017-Z/METRICS.

M. Garg, M. Malhotra, and H. Singh, “A novel machine-learning framework-based on LBP and GLCM approaches for CBIR system,” International Arab Journal of Information Technology, vol. 18, no. 3, pp. 297–305, 2021, doi: 10.34028/IAJIT/18/3/5.

A. H. Rangkuti, J. M. Kerta, and A. H. Aslamiah, “Utilization of Mulwin-LBP Algorithm To Support Batik Image Classification,” J Theor Appl Inf Technol, vol. 99, no. 24, pp. 6309–6318, 2021.

E. M. Martey, H. Lei, X. Li, and O. Appiah, “Effective Image Representation using Double Colour Histogram for Content-Based Image Retrieval,” Informatica (Slovenia), vol. 45, no. 7, pp. 97–105, 2021, doi: 10.31449/inf.v45i7.3715.

H. Prasetyo and J. W. Simatupang, “Batik Image Retrieval Using Maximum Run Length LBP and Sine-Cosine Optimizer,” in ICSECC 2019 - International Conference on Sustainable Engineering and Creative Computing: New Idea, New Innovation, Proceedings, 2019, pp. 265–269. doi: 10.1109/ICSECC.2019.8907190.

S. Dhingra and P. Bansal, “Experimental analogy of different texture feature extraction techniques in image retrieval systems,” Multimed Tools Appl, vol. 79, no. 37–38, pp. 27391–27406, Oct. 2020, doi: 10.1007/S11042-020-09317-3/METRICS.

S. K. Kanaparthi, U. S. N. Raju, P. Shanmukhi, G. K. Aneesha, and M. E. U. Rahman, “Image Retrieval by Integrating Global Correlation of Color and Intensity Histograms with Local Texture Features,” Multimed Tools Appl, vol. 79, no. 47–48, pp. 34875–34911, Dec. 2020, doi: 10.1007/S11042-019-08029-7/METRICS.

D. Latha and A. Geetha, “Effective CBIR based on hybrid image features and multilevel approach,” Multimed Tools Appl, vol. 81, no. 20, pp. 28559–28582, Aug. 2022, doi: 10.1007/S11042-022-12588-7/METRICS.

D. Srivastava, B. Rajitha, S. Agarwal, and S. Singh, “Pattern-based image retrieval using GLCM,” Neural Comput Appl, vol. 32, no. 15, pp. 10819–10832, Aug. 2020, doi: 10.1007/S00521-018-3611-1/METRICS.

N. Varish, “A modified similarity measurement for image retrieval scheme using fusion of color, texture and shape moments,” Multimed Tools Appl, vol. 81, no. 15, pp. 20373–20405, Jun. 2022, doi: 10.1007/S11042-022-12289-1/METRICS.

B. P. H. K. M. D. Senarathna and R. M. T. P. Rajakaruna, “Feature Descriptor for Sri Lankan Batik Patterns Using Hu Moment Invariants and GLCM,” 2021 10th International Conference on Information and Automation for Sustainability, ICIAfS 2021, pp. 197–202, Aug. 2021, doi: 10.1109/ICIAFS52090.2021.9606106.

E. Winarno, W. Hadikurniawati, A. Septiarini, and H. Hamdani, “Analysis of color features performance using support vector machine with multi-kernel for batik classification,” International Journal of Advances in Intelligent Informatics, vol. 8, no. 2, pp. 151–164, Jul. 2022, doi: 10.26555/ijain.v8i2.821.

F. Budiman, “SVM-RBF parameters testing optimization using cross validation and grid search to improve multiclass classification,” Scientific Visualization, vol. 11, no. 1, pp. 80–90, 2019, doi: 10.26583/sv.11.1.07.

D. Trimakno and Kusrini, “Impact of Augmentation on Batik Classification using Convolution Neural Network and K-Neareast Neighbor,” ICOIACT 2021 - 4th International Conference on Information and Communications Technology: The Role of AI in Health and Social Revolution in Turbulence Era, pp. 285–289, 2021, doi: 10.1109/ICOIACT53268.2021.9564000.

B. Khaldi, O. Aiadi, and K. M. Lamine, “Image representation using complete multi-texton histogram,” Multimed Tools Appl, vol. 79, no. 11–12, pp. 8267–8285, Mar. 2020, doi: 10.1007/S11042-019-08350-1/METRICS.

I. Nurhaida, V. Ayumi, D. Fitrianah, R. A. M. Zen, H. Noprisson, and H. Wei, “Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition,” International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 2, pp. 2045–2053, Apr. 2020, doi: 10.11591/IJECE.V10I2.PP2045-2053.

M. A. Rasyidi and T. Bariyah, “Batik pattern recognition using convolutional neural network,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 4, pp. 1430–1437, Aug. 2020, doi: 10.11591/EEI.V9I4.2385.

B. S. Negara, E. Satria, S. Sanjaya, and D. R. Dwi Santoso, “ResNet-50 for Classifying Indonesian Batik with Data Augmentation,” 2021 International Congress of Advanced Technology and Engineering, ICOTEN 2021, Jul. 2021, doi: 10.1109/ICOTEN52080.2021.9493488.

F. A. Putra et al., “Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method,” 2021 6th International Conference on Informatics and Computing, ICIC 2021, 2021, doi: 10.1109/ICIC54025.2021.9632937.

A. E. Minarno, T. D. Antoko, and Y. Azhar, “Generation of Batik Patterns Using Generative Adversarial Network with Content Loss Weighting,” Int J Adv Sci Eng Inf Technol, vol. 13, no. 1, p. 348, Jan. 2023, doi: 10.18517/IJASEIT.13.1.16201.

A. E. Minarno, M. Y. Hasanuddin, and Y. Azhar, “Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor,” JOIV : International Journal on Informatics Visualization, vol. 7, no. 1, pp. 115–121, Feb. 2023, doi: 10.30630/JOIV.7.1.1299.

A. E. Minarno, F. D. S. Sumadi, Y. Munarko, W. Y. Alviansyah, and Y. Azhar, “Image Retrieval using Multi Texton Co-occurrence Descriptor and Discrete Wavelet Transform,” 2020 8th International Conference on Information and Communication Technology, ICoICT 2020, Jun. 2020, doi: 10.1109/ICOICT49345.2020.9166361.

A. E. Minarno, F. D. S. Sumadi, H. Wibowo, and Y. Munarko, “Classification of batik patterns using K-Nearest neighbor and support vector machine,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 3, pp. 1260–1267, Jun. 2020, doi: 10.11591/EEI.V9I3.1971.

D. G. T. Meranggi, N. Yudistira, and Y. A. Sari, “Batik Classification Using Convolutional Neural Network with Data Improvements,” JOIV : International Journal on Informatics Visualization, vol. 6, no. 1, pp. 6–11, Mar. 2022, doi: 10.30630/JOIV.6.1.716.

M. Fadhilla, D. Suryani, N. Syafitri, and H. Gunawan, “Image Retrieval of Indonesian Batik Clothing Based on Convolutional Neural Network,” in Proceedings of the International Conference on Electrical Engineering and Informatics, 2022, pp. 177–180. doi: 10.1109/IConEEI55709.2022.9972332.

H. Prasetyo and B. A. Putra Akardihas, “Batik image retrieval using convolutional neural network,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 17, no. 6, pp. 3010–3018, 2019, doi: 10.12928/TELKOMNIKA.v17i6.12701.

E. M. Martey, H. Lei, X. Li, and O. Appiah, “Image Representation Using Stacked Colour Histogram,” Algorithms, vol. 14, no. 8, 2021.

A. E. Minarno, Y. Azhar, F. D. Setiawan Sumadi, and Y. Munarko, “A Robust Batik Image Classification using Multi Texton Co-Occurrence Descriptor and Support Vector Machine,” 2020 3rd International Conference on Intelligent Autonomous Systems, ICoIAS 2020, pp. 51–55, Feb. 2020, doi: 10.1109/ICOIAS49312.2020.9081833.

C. Irawan, E. N. Ardyastiti, D. R. I. M. Setiadi, E. H. Rachmawanto, and C. A. Sari, “A survey: Effect of the number of GLCM features on classification accuracy of lasem batik images using K-nearest neighbor,” 2018 International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2018, pp. 33–38, Nov. 2018, doi: 10.1109/ISRITI.2018.8864443.

A. E. Minarno, I. Soesanti, and H. A. Nugroho, “Batik Nitik 960 Dataset for Classification, Retrieval, and Generator,” Data 2023, Vol. 8, Page 63, vol. 8, no. 4, p. 63, Mar. 2023, doi: 10.3390/DATA8040063.

A. E. Minarno, I. Soesanti, and H. A. Nugroho, “Batik Nitik 960 Dataset for Classification, Retrieval, and Generator,” Data 2023, Vol. 8, Page 63, vol. 8, no. 4, p. 63, Mar. 2023, doi: 10.3390/DATA8040063.