The Use of Deep Learning in Image Segmentation and Classification
DOI:
https://doi.org/10.61841/m99r7q84Keywords:
Classification Image, Segmentation, Deep Learning, Processing, Neural Network, PixelsAbstract
This paper proposed image classification and segmentation techniques using deep learning. Image classification is an advanced method that will be affected by several factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and therefore the techniques used for improving classification accuracy. Additionally, some necessary problems affecting classification performance are mentioned. This literature review suggests that planning a suitable image processing procedure could be a requirement for the successful classification of remotely sensed information into a thematic map. Effective use of multiple options of remotely sensed data and therefore the selection of an appropriate. Image segmentation refers to the partition of an image into completely different regions that are homogeneous or similar and heterogeneous in some characteristics. During a preprocessing stage, an image is over segmented into super pixels by the normalized cut algorithm. Using the various algorithms the present methodologies of image segmentation are reviewed so that user interaction is possible for images. Strategies of image analysis belong to a general knowledge base area of the multidimensional signal process.
Downloads
References
[1] S. Gollapudi and S. Gollapudi, “Deep Learning for Computer Vision,” in Learn Computer Vision Using
OpenCV, 2019.
[2] G. Wang et al., “Interactive Medical Image Segmentation Using Deep Learning with Image-Specific Fine
Tuning,” IEEE Trans. Med. Imaging, 2018.
[3] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the
IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016.
[4] G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis. 2017.
[5] Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A
review,” Neurocomputing, 2016.
[6] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd
International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2015.
[7] A. Janowczyk and A. Madabhushi, “Deep learning for digital pathology image analysis: A comprehensive
tutorial with selected use cases,” J. Pathol. Inform., 2016.
[8] J. Ker, L. Wang, J. Rao, and T. Lim, “Deep Learning Applications in Medical Image Analysis,” IEEE Access,
2017.
[9] B. Zhao, J. Feng, X. Wu, and S. Yan, “A survey on deep learning-based fine-grained object classification and
semantic segmentation,” International Journal of Automation and Computing. 2017
[10] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015.
[11] K. Suzuki, “Overview of deep learning in medical imaging,” Radiological Physics and Technology. 2017.
[12] M. Bai and R. Urtasun, “Deep watershed transform for instance segmentation,” in Proceedings - 30th IEEE
Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017.
[13] L. Ruff et al., “Deep one-class classification,” in 35th International Conference on Machine Learning, ICML
2018, 2018.
[14] C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. Jorge Cardoso, “Generalised dice overlap as a deep
learning loss function for highly unbalanced segmentations,” in Lecture Notes in Computer Science
(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017.
[15] M. A. Rahman and Y. Wang, “Optimizing intersection-over-union in deep neural networks for image
segmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics), 2016.
[16] A. Shaban, S. Bansal, Z. Liu, I. Essa, and B. Boots, “One-shot learning for semantic segmentation,” in British
Machine Vision Conference 2017, BMVC 2017, 2017.
[17] J. H. Metzen, M. C. Kumar, T. Brox, and V. Fischer, “Universal Adversarial Perturbations Against Semantic
Image Segmentation,” in Proceedings of the IEEE International Conference on Computer Vision, 2017.
[18] A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image
classification,” IEEE Trans. Geosci. Remote Sens., 2016.
[19] E. Simo-Serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua, and F. Moreno-Noguer, “Discriminative learning of
deep convolutional feature point descriptors,” in Proceedings of the IEEE International Conference on
Computer Vision, 2015.
[20] G. L. Oliveira, A. Valada, C. Bollen, W. Burgard, and T. Brox, “Deep learning for human part discovery in
images,” in Proceedings - IEEE International Conference on Robotics and Automation, 2016.
[21] C. Wachinger, M. Reuter, and T. Klein, “DeepNAT: Deep convolutional neural network for segmenting
neuroanatomy,” Neuroimage, 2018.
[22] M. Gygli, M. Norouzi, and A. Angelova, “Deep value networks learn to evaluate and iteratively refine
structured outputs,” in 34th International Conference on Machine Learning, ICML 2017, 2017.
[23] V. Fischer, M. C. Kumar, J. H. Metzen, and T. Brox, “Adversarial examples for semantic image
segmentation,” in 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track
Proceedings, 2019.
[24] Y. Li, H. Zhang, X. Xue, Y. Jiang, and Q. Shen, “Deep learning for remote sensing image classification: A
survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2018.
[25] A. Madabhushi and G. Lee, “Image analysis and machine learning in digital pathology: Challenges and
opportunities,” Medical Image Analysis, 2016.
[26] A. Van Opbroek, M. A. Ikram, M. W. Vernooij, and M. De Bruijne, “Transfer learning improves supervised
image segmentation across imaging protocols,” IEEE Trans. Med. Imaging, 2015
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
