Automatic Classification of Blue and White Porcelain Sherds Based on Data Augmentation and Feature Fusion

References

• Abadi, M., M. Khoudeir, and S. Marchand. 2012. Gabor filter-based texture features to archaeological ceramic materials characterization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7340:164–184. doi:10.1007/978-3-642-31254-0_38.
   Google Scholar
• Chen, P., S. Liu, H. Zhao and J. Jia. 2020. Gridmask data augmentation. arXiv Preprint arXiv:2001.04086.
   Google Scholar
• Chetouani, A., T. Debroutelle, S. Treuillet, M. Exbrayat, and S. Jesset. 2018. Classification of ceramic shards based on convolutional neural network. Proceedings - International Conference on Image Processing, ICIP, Athens, Greece, 1038–42. doi:10.1109/ICIP.2018.8451728.
   Google Scholar
• Debroutelle, T., S. Treuillet, A. Chetouani, M. Exbrayat, L. Martin, and S. Jesset. 2017. Automatic classification of ceramic sherds with relief motifs. Journal of Electronic Imaging 26 (2):23010. doi:10.1117/1.jei.26.2.023010.
   Web of Science ®Google Scholar
• DeVries, T., and G. W. Taylor. 2017. Improved regularization of convolutional neural networks with cutout. arXiv Preprint arXiv:1708.04552.
   Google Scholar
• Gong, C., D. Wang, M. Li, V. Chandra, and Q. Liu. 2021. KeepAugment: A simple information-preserving data augmentation approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Online, 1055–64.
   Google Scholar
• Han, C., K. Murao, S. Satoh and H. Nakayama. 2019. Learning more with less: Gan-based medical image augmentation. Medical Imaging Technology 37 (3):137–42
   Google Scholar
• Hanzaei, S. H., A. Afshar, and F. Barazandeh. 2017. Automatic detection and classification of the ceramic tiles’ surface defects. Pattern Recognition 66:174–89. doi:10.1016/j.patcog.2016.11.021.
   Web of Science ®Google Scholar
• He, K., X. Zhang, S. Ren and J. Sun. 2016. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, 770–78. doi:10.1109/CVPR.2016.90.
   Google Scholar
• He, T., Z. Zhang, H. Zhang, Z. Zhang, J. Xie and M. Li. 2019. Bag of tricks for image classification with convolutional neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 558–67.
   Google Scholar
• Jiangsu Provincial Study Society For Ancient Ceramics. 2010. Flower and bird roll. Vol.1 of Chinese blue and white porcelain patterns, ed. L. Shaobin, D. Wang, D. Zhou, and S. Xing, 13–183. NanJing, China: Southeast University Press.
   Google Scholar
• Li, M., S. Lian, F. Wang, Y. Zhou, B. Chen, L.Guan and Y. Wu. 2020. Neural network modeling based double-population chaotic accelerated particle swarm optimization and diffusion theory for solubility prediction. Chemical Engineering Research and Design 2020, 155:98–107. doi:10.1016/j.cherd.2020.01.003.
   Google Scholar
• Li, P., X. Li, and X. Long. 2020. FenceMask: A Data augmentation approach for pre-extracted image features. arXiv Preprint arXiv:2006.07877.
   Google Scholar
• Liu, X., K. H. Ghazali, F. Han, and I. I. Mohamed. 2020. Automatic detection of oil palm tree from UAV images based on the deep learning method. Applied Artificial Intelligence 35 (1):13–24. doi:10.1080/08839514.2020.1831226.
   Web of Science ®Google Scholar
• Mukhoti, J., S. Dutta, and R. Sarkar. 2020. Handwritten digit classification in bangla and hindi using deep learning. Applied Artificial Intelligence 34 (14):1074–99. doi:10.1080/08839514.2020.1804228.
   Web of Science ®Google Scholar
• Shin, H. C., N. A. Tenenholtz, J. K. Rogers, C. G. Schwarz, M. L. Senjem, J. L. Gunter, K. P. Andriole and M. Michalski. 2018. Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In International Workshop on Simulation and Synthesis in Medical Imaging, 1–11. doi:10.1007/978-3-030-00536-8_1.
   Google Scholar
• Simonyan, K., and A. Zisserman 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
   Google Scholar
• Singh, K. K., H. Yu, A. Sarmasi, G. Pradeep and Y. J. Lee. 2018. Hide-and-seek: A data augmentation technique for weakly-supervised localization and beyond. arXiv Preprint arXiv:1811.02545.
   Google Scholar
• Smith, P., D. Bespalov, S. Ali and P. Jeppson. 2010. Classification of archaeological ceramic fragments using texture and color descriptors. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW, San Francisco, USA, 49–54. doi:10.1109/CVPRW.2010.5543523.
   Google Scholar
• Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich. 2015. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, USA, 1–9.
   Google Scholar
• Taheri, S., and Ö. Toygar. 2019. Integrating feature extractors for the estimation of human facial age. Applied Artificial Intelligence 33 (5):379–98. doi:10.1080/08839514.2019.1577009.
   Web of Science ®Google Scholar
• Thepade, S. D., and P. R. Chaudhari. 2020. Land usage identification with fusion of thepade SBTC and sauvola thresholding features of aerial images using ensemble of machine learning algorithms. Applied Artificial Intelligence 35 (2):154–70. doi:10.1080/08839514.2020.1842627.
   Web of Science ®Google Scholar
• Yun, S., D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo. 2019. Cutmix: Regularization strategy to train strong classifiers with localizable features. Proceedings of the IEEE/CVF International Conference on Computer Vision, Long Beach, USA, 6023–32.
   Google Scholar
• Zhang, H., M. Cisse, Y. N. Dauphin and D. Lopez-Paz. 2017. mixup: Beyond empirical risk minimization. arXiv Preprint arXiv:1710.09412.
   Google Scholar
• Zhong, Z., L. Zheng, G. Kang, S. Li, Y. Yang. 2020. Random erasing data augmentation. Proceedings of the AAAI Conference on Artificial Intelligence 34 (7):13001–08. doi:10.1609/aaai.v34i07.7000.
   Google Scholar
• Zhou, B., A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. 2016. Learning deep features for discriminative localization. Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, 2921–29.
   Google Scholar
• Zhu, J. Y., T. Park, P. Isola, and A. A. Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE international conference on computer vision, Honolulu, USA, 2223–32.
   Google Scholar