Swin Tabanlı Dönüştürülmüş Görüntülerin Sınıflandırılması

Yıl 2023, , 108 - 115, 31.08.2023

Serdar Çiftçi

https://doi.org/10.46578/humder.1333782

Öz

Görüntü sınıflandırma bilgisayarlı görü alanındaki temel çalışmalardan biridir. Görüntü çözünürlüğü ve görüntünün netliği sınıflandırma performansını önemli ölçüde etkileyen faktörlerdendir. Bu çalışmada görüntülerin çözünürlüğünün ve netliğinin Swin tabanlı dönüştürücü olan Swin2SR algoritması kullanılarak artırılmasıyla görüntü sınıflandırma performansı incelenmiştir. Sınıflandırma için transfer öğrenme olarak ResNet18 modeli kullanılmıştır. CIFAR10 veri kümesi üzerinde 50 epok için yapılan deneyler sonucunda Swin2SR algoritmasının görüntülerin çözünürlüğünü ve netliğini artırarak sınıflandırma doğruluğunu %85’ten %87’ye çıkardığı gözlemlenmiştir.

Anahtar Kelimeler

Görüntü Sınıflandırma, Swin2SR Dönüştürme, Transfer Öğrenme

Kaynakça

• A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems, 25, 2012.
• G. Huang, Z. Liu, L. Van Der Maaten, K. Q. Weinberger, Densely Connected Convolutional Networks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4700-4708), 2017.
• K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778), 2016.
• C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going Deeper with Convolutions, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-9), 2015.
• A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv preprint arXiv:1704.04861, 2017.
• K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-scale Image Recognition, arXiv preprint arXiv:1409.1556, 2014.
• S. J. Pan, Q. Yang, A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359, 2009. C. Shorten, T. M. Khoshgoftaar, A Survey on Image Data Augmentation for Deep Learning, Journal of Big Data, 6(1), 1-48, 2019.
• H. Chen, Y. Pei, H. Zhao, Y. Huang, Super-resolution Guided Knowledge Distillation for Low-resolution Image Classification, Pattern Recognition Letters, 155, 62-68, 2022.
• S. Hao, W. Wang, Y. Ye, E. Li, L. Bruzzone, A Deep Network Architecture for Super-resolution-aided Hyperspectral Image classification with Classwise Loss. IEEE Transactions on Geoscience and Remote Sensing, 56(8), 4650-4663, 2018.
• F. M. Senalp, M. Ceylan, Effects of the Deep Learning-based Super-resolution Method on Thermal Image Classification Applications, Multimedia Tools and Applications, 81(7), 9313-9330, 2022.
• J. D. Van Ouwerkerk, Image Super-resolution Survey, Image and Vision Computing, 24(10), 1039-1052, 2006.
• K. Nasrollahi, T. B. Moeslund, Super-resolution: A Comprehensive Survey, Machine Vision and Applications, 25, 1423-1468, 2014.
• Z. Wang, J. Chen, S. C. Hoi, Deep Learning for Image Super-resolution: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10), 3365-3387, 2020.
• C. Dong, C. C. Loy, K. He, X. Tang, Image Super-resolution Using Deep Convolutional Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 295-307, 2015.
• C. Tian, X. Zhang, J. C. W. Lin, W. Zuo, Y. Zhang, C. W. Lin, Generative Adversarial Networks for Image Super-resolution: A Survey, arXiv preprint arXiv:2204.13620, 2022.
• Z. Lu, J. Li, H. Liu, C. Huang, L. Zhang, T. Zeng, Transformer for Single Image Super-resolution, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 457-466), 2022.
• A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, Attention is All You Need, Advances in Neural Information Processing Systems, 30, 2017.
• A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, arXiv preprint arXiv:2010.11929, 2020.
• S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, M. Shah, Transformers in Vision: A Survey, ACM Computing Surveys (CSUR), 54(10s), 1-41, 2022.
• M. V. Conde, U. J. Choi, M. Burchi, R. Timofte, Swin2SR: Swinv2 Transformer for Compressed Image Super-resolution and Restoration, In European Conference on Computer Vision (pp. 669-687), Cham: Springer Nature Switzerland, 2022.
• Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows, In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012-10022), 2021.
• A. Krizhevsky, G. Hinton, Learning Multiple Layers of Features from Tiny Images, 2009.