Semantic segmentation of pendet dance images using multires U-Net architecture


Hendri Ramdan(1*); Moh. Arief Soeleman(2); Purwanto Purwanto(3); Bahtiar Imran(4); Ricardus Anggi Pramunendar(5);

(1) Dian Nuswantoro University
(2) Dian Nuswantoro University
(3) Dian Nuswantoro University
(4) Mataram Technology of University
(5) Dian Nuswantoro University
(*) Corresponding Author

  

Abstract


As a cultural heritage, traditional dance must be protected and preserved. Pendet dance is a traditional dance from Bali, Indonesia. Dance recognition raises a complex problem for computer vision research because the features representing the dancer must focus on the dancer's entire body. This can be done by performing a segmentation task process. One type of segmentation task in computer vision is the semantic segmentation. Mask R-CNN and U-NET were employed in this task. Since it was first introduced in 2015, semantic segmentation using the U-Net architecture has been widely adopted, developed, and modified. One of the new architectures applied is the MultiRes UNet. This study carries out a semantic segmentation task on the Balinese Pendet dance image using the MultiRes UNet architecture by changing the value of α (alpha) to obtain the best results. This architectural is evaluated by DC score, Jaccard index, and MSE. In this dataset, the alpha value of 1.9 resulted in the best score for DC and the Jaccard index with 98.47% and 99.23% respectively. On the other hand, an alpha value of 1.8 obtained the best score of MSE with 8.20E-04.

Keywords


Semantic Segmentation; U-Net; MultiRes Unet; Deep Learning; Balinese Pendet Dance.

  
  

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doi  https://doi.org/10.33096/ilkom.v14i3.1316.329-338 		  

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C. Cui, J. Li, D. Du, H. Wang, P. Tu, and T. Cao, “The method of dance movement segmentation and labanotation generation based on rhythm,” IEEE Access, vol. 9, pp. 31213–31224, 2021, doi: 10.1109/ACCESS.2021.3060103.

L. Y. MENG and Y. KARULUS, “the Indonesia-Malaysia Cultural Heritage Disputes: a Case Study of the Pendet Dance and Rasa Sayange Folk Song,” MANU J. Pus. Penataran Ilmu dan Bhs., no. July, 2019, doi: 10.51200/manu.v0i0.1880.

G. A. M. Puspawati and L. De Liska, “Nilai-Nilai Pendidikan Karakter dalam ragam Gerak tari Pendet,” Stilistika J. Pendidik. Bhs. dan Seni, vol. 7, no. 2, pp. 274–291, 2019, doi: 10.5281/zenodo.3900648.

K. V. V. Kumar, P. V. V. Kishore, and D. Anil Kumar, “Indian Classical Dance Classification with Adaboost Multiclass Classifier on Multifeature Fusion,” Math. Probl. Eng., vol. 2017, 2017, doi: 10.1155/2017/6204742.

K. V. V. Kumar and P. V. V. Kishore, “Indian classical dance mudra classification using HOG features and SVM Classifier,” Int. J. Electr. Comput. Eng., vol. 7, no. 5, pp. 2537–2546, 2017, doi: 10.11591/ijece.v7i5.pp2537-2546.

R. A. Pramunendar, D. P. Prabowo, D. Pergiwati, Y. Sari, P. N. Andono, and M. A. Soeleman, “New workflow for marine fish classification based on combination features and CLAHE enhancement technique,” Int. J. Intell. Eng. Syst., vol. 13, no. 4, pp. 293–304, 2020, doi: 10.22266/IJIES2020.0831.26.

F. Sun, V. Ajith Kumar, G. Yang, A. Zhang, and Y. Zhang, “Circle-u-net: An efficient architecture for semantic segmentation,” Algorithms, vol. 14, no. 6, 2021, doi: 10.3390/a14060159.

N. Ibtehaz and M. S. Rahman, “MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation,” Neural Networks, vol. 121, pp. 74–87, 2020, doi: 10.1016/j.neunet.2019.08.025.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9351, pp. 234–241. doi: 10.1007/978-3-319-24574-4_28.

Q. Zuo, S. Chen, and Z. Wang, “R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation,” Secur. Commun. Networks, vol. 2021, 2021, doi: 10.1155/2021/6625688.

M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari1, “Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation,” 2018, doi: 10.1007/978-3-030-47436-2_16.

V. Ashkani Chenarlogh et al., “Clinical target segmentation using a novel deep neural network: double attention Res-U-Net,” Sci. Rep., vol. 12, no. 1, pp. 1–17, 2022, doi: 10.1038/s41598-022-10429-z.

H. Cao et al., “Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation,” pp. 1–14, 2021, [Online]. Available: http://arxiv.org/abs/2105.05537

D. Maji, P. Sigedar, and M. Singh, “Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors,” Biomed. Signal Process. Control, vol. 71, no. PA, p. 103077, 2022, doi: 10.1016/j.bspc.2021.103077.

K. B. Soulami, N. Kaabouch, M. N. Saidi, and A. Tamtaoui, “Breast cancer: One-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation,” Biomed. Signal Process. Control, vol. 66, no. November 2020, p. 102481, 2021, doi: 10.1016/j.bspc.2021.102481.

Z. Wang, Y. Zou, and P. X. Liu, “Hybrid dilation and attention residual U-Net for medical image segmentation,” Comput. Biol. Med., vol. 134, no. April, p. 104449, 2021, doi: 10.1016/j.compbiomed.2021.104449.

K. Sanjar, O. Bekhzod, J. Kim, J. Kim, A. Paul, and J. Kim, “Improved U-net: Fully convolutional network model for skin-lesion segmentation,” Appl. Sci., vol. 10, no. 10, pp. 1–14, 2020, doi: 10.3390/app10103658.

N. S. Punn and S. Agarwal, “Inception U-Net Architecture for Semantic Segmentation to Identify Nuclei in Microscopy Cell Images,” ACM Trans. Multimed. Comput. Commun. Appl., vol. 16, no. 1, 2020, doi: 10.1145/3376922.

Y. Cai and Y. Wang, “MA-Unet: an improved version of Unet based on multi-scale and attention mechanism for medical image segmentation,” p. 16, 2022, doi: 10.1117/12.2628519.

J. Zhang, Y. Jin, J. Xu, X. Xu, and Y. Zhang, “MDU-Net: multi-scale densely connected u-net for biomedical image segmentation,” 2018, [Online]. Available: http://arxiv.org/abs/1812.00352

Y. Weng, T. Zhou, Y. Li, and X. Qiu, “NAS-Unet: Neural architecture search for medical image segmentation,” IEEE Access, vol. 7, no. c, pp. 44247–44257, 2019, doi: 10.1109/ACCESS.2019.2908991.

S. Piao and J. Liu, “Accuracy improvement of UNet based on dilated convolution,” in Journal of Physics: Conference Series, 2019, vol. 1345, no. 5. doi: 10.1088/1742-6596/1345/5/052066.

A. Abdollahi, B. Pradhan, and A. M. Alamri, “An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images,” Geocarto Int., vol. 0, no. 0, p. 000, 2020, doi: 10.1080/10106049.2020.1856199.

M. Lei, Z. Rao, H. Wang, Y. Chen, L. Zou, and H. Yu, “Maceral groups analysis of coal based on semantic segmentation of photomicrographs via the improved U-net,” Fuel, vol. 294, no. June 2020, p. 120475, 2021, doi: 10.1016/j.fuel.2021.120475.

Z. Wang et al., “Semantic segmentation and analysis on sensitive parameters of forest fire smoke using smoke-unet and landsat-8 imagery,” Remote Sens., vol. 14, no. 1, 2022, doi: 10.3390/rs14010045.

B. Imran, “Classification of Lombok songket cloth image using Convolution Neural Network Method ( CNN ),” no. 85, pp. 149–156, 2020, doi: 10.33480/pilar.v17i2.2705.

K. Armanious, V. Kumar, S. Abdulatif, T. Hepp, S. Gatidis, and B. Yang, “IpA-MedGAN: Inpainting of Arbitrary Regions in Medical Imaging,” in Proceedings - International Conference on Image Processing, ICIP, 2020, vol. 2020-Octob, pp. 3005–3009. doi: 10.1109/ICIP40778.2020.9191207.

C. Yan, X. Fan, J. Fan, and N. Wang, “Improved U-Net remote sensing classification algorithm based on Multi-Feature Fusion Perception,” pp. 1–18, 2022.

A. Abdollahi and B. Pradhan, “Integrating semantic edges and segmentation information for building extraction from aerial images using UNet,” Mach. Learn. with Appl., vol. 6, no. October, p. 100194, 2021, doi: 10.1016/j.mlwa.2021.100194.

Z. Alom, C. Yakopcic, T. M. Taha, and V. K. Asari, “Nuclei Segmentation with recurrent residual convolutional Neural Networks based U-Net,” NAECON 2018 - IEEE Natl. Aerosp. Electron. Conf., pp. 228–233, 2018.


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Copyright (c) 2022 Hendri Ramdan, Moh. Arief Soeleman, Purwanto Purwanto, Bahtiar Imran, Ricardus Anggi Pramunendar

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