Balinese Mask Characters Classification using Bag of Visual Words Model
Abstract
Mask, often known by Balinese as “Tapel”, is made of pule wood. It depicts the representation of characters in the “badbad” or legend. Bali has many types of mask dances that are often performed, which makes tourists interested in visiting Bali. Unfortunately, many tourists do not know the information contained in Balinese masks. The most important information contained in the character of the Balinese masks. The characters of each mask are different even though they have the same type. As mask art is also a cultural heritage from generation to generation, it needs to be preserved. It is necessary to have information in the form of technology that can distinguish the characters from Balinese masks. In this study, bag of visual word method in the classification process of Balinese mask characters is used, where in this method, there are several algorithms used, namely SURF as feature detection, K-Means as a clustering process to get the value of feature quantization, and SVM as a classification of Balinese mask character. The result of the accuracy level obtained from the testing process is 80%.
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References
[2] I. W. Suardana, “75 struktur rupa topeng bali klasik,” vol. 4, no. 1, 2006.
[3] J. Witabora, K. S. Adidharma, L. C. Luzar, M. Meilani, and N. Soedarso, “Usability, Design, and Content Issues of Mobile Apps for Indonesia Cultural Art Promotion: A Balinese Mask,” Humaniora, vol. 7, no. 4, p. 427, 2016, doi: 10.21512/humaniora.v7i4.3596.
[4] N. Singhal, N. Singhal, and V. Kalaichelvi, “Image classification using bag of visual words model with FAST and FREAK,” Proc. 2017 2nd IEEE Int. Conf. Electr. Comput. Commun. Technol. ICECCT 2017, 2017, doi: 10.1109/ICECCT.2017.8117861.
[5] H. K. Suh, J. W. Hofstee, J. Ijsselmuiden, and E. J. Van Henten, “ScienceDirect Sugar beet and volunteer potato classification using Bag-of-Visual-Words model , Scale-Invariant Feature Transform , or Speeded Up Robust Feature descriptors and crop row information,” Biosyst. Eng., pp. 1–17, 2017, doi: 10.1016/j.biosystemseng.2017.11.015.
[6] B. Anand, “Face Recognition using SURF Features and SVM Classifier,” vol. 8, no. 1, pp. 1–8, 2016.
[7] X. Wang, X. Lv, L. Li, G. Cui, and Z. Zhang, “A New Method of Speeded Up Robust Features Image Registration Based on Image Preprocessing,” 2018 Int. Conf. Inf. Syst. Comput. Aided Educ., pp. 317–321, 2018.
[8] M. Paul, N. I. T. Silchar, and N. I. T. Silchar, “Image Hashing based on Shape Context and Speeded Up Robust Features ( SURF ),” 2019 Int. Conf. Autom. Comput. Technol. Manag., pp. 464–468, 2019.
[9] X. Zhang, K. Jiang, Y. Zheng, J. An, Y. Hu, and L. Jiao, “Spatially Constrained Bag-of-Visual-Words for Hyperspectral Image Classification,” pp. 501–504, 2016.
[10] V. Mehra, “Hand Gesture Recognition using Support Vector Machine and Bag of Visual Words model Feature Selection / Engineering,” pp. 5–8, 2018.
[11] H. Kato and T. Harada, “Image reconstruction from bag-of-visual-words,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 955–962, 2014, doi: 10.1109/CVPR.2014.127.
[12] M. Yao and C. Zhu, “SVM and Adaboost-based classifiers with fast PCA for face reocognition,” 2016 IEEE Int. Conf. Consum. Electron. ICCE-China 2016, pp. 0–4, 2017, doi: 10.1109/ICCE-China.2016.7849742.
[13] B. U. Umar, J. Agajo, O. M. Olaniyi, A. Aliyu, O. S. Owolabi, and J. G. Kolo, “Human Detection Using Speeded-Up Robust Features and Support Vector Machine from Aerial,” 2017.
[14] E. Kremic and A. Subasi, “Performance of random forest and SVM in face recognition,” Int. Arab J. Inf. Technol., vol. 13, no. 2, pp. 287–293, 2016.
[15] R. Senthilkumar and R. K. Gnanamurthy, “Performance improvement in classification rate of appearance based statistical face recognition methods using SVM classifier,” 2017 4th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2017, 2017, doi: 10.1109/ICACCS.2017.8014584.
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