Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Unsupervised Transudative TL Feature Learning for Image Feature Extraction and Representation


Affiliations
1 Department of Information Technology, St. Joseph College of Engineering, India
2 Department of Electronics and Instrumentation Engineering, Kamaraj College of Engineering and Technology, India
3 Department of Computer Science and Application, Odisha University of Agriculture and Technology, India
4 Department of Artificial Intelligence and Machine Learning, Shri Ramdeobaba College of Engineering and Management, India
     

   Subscribe/Renew Journal


In this study, we address the problem of unsupervised transductive transfer learning for image feature extraction and representation. While transfer learning has shown promising results in various domains, its application to image feature extraction in an unsupervised transductive setting remains relatively unexplored. The research gap lies in the scarcity of methods that can effectively learn meaningful image representations without access to labeled data in the target domain, hindering the broader applicability of transfer learning in computer vision. Our research seeks to bridge this gap by proposing a novel framework that leverages unsupervised feature learning to enhance the adaptability of models across different image domains, thus contributing to the advancement of transfer learning in the field of computer vision. Experimental results demonstrate the effectiveness of our method in addressing this critical research gap and its potential for real-world applications.

Keywords

Unsupervised, Transductive Transfer Learning, Image Feature Extraction, Representation, Deep Neural Networks
Subscription Login to verify subscription
User
Notifications
Font Size

  • Luay Fraiwan and Mohanad Alkhodari, “Neonatal Sleep Stage Identification using Long Short-Term Memory Learning System”, Proceedings of International Conference on Medical and Biological Engineering, pp. 1-14, 2020.
  • Adrien Depeursinge, “Multiscale and Multidirectional Biomedical Texture Analysis”, Proceedings of International Conference on Biomedical Texture Analysis, pp. 231-236, 2017.
  • C.C. Hung, E. Song and Y. Lan, “Image Texture, Texture Features, and Image Texture Classification”, Proceedings of International Conference on Image Texture Analysis, pp. 3- 14, 2019.
  • William Henry Nailon, “Texture Analysis Methods for Medical Image Characterisation”, Master Thesis, Department of Oncology Physics, Edinburgh Cancer Centre and School of Engineering, University of Edinburgh, pp. 1- 122, 2016.
  • Godliver Owomugisha, Friedrich Melchert, Ernest Mwebaze, John A Quinn and Michael Biehl, “Machine Learning for Diagnosis of Disease in Plants using Spectral Data”, Proceedings of International Conference on Artificial Intelligence, pp. 334-339, 2018.
  • K. Anastraj, T. Chakravarthy and T. Poondi, “Breast Cancer Detection Either Benign or Malignant Tumor using Deep Convolutional Neural Network with Machine Learning Techniques”, Proceedings of International Conference on Computational Techniques, Electronics and Mechanical Systems, pp. 566-573, 2018.
  • Jisha Jose and S. Sureshkumar, “Tuna Classification using Super Learner Ensemble of Region-Based CNN-Grouped 2D-LBP Models”, Information Processing in Agriculture, Vol. 9, pp. 1-13, 2021.
  • Xiaopei Liu, Zhaoyang Lu, Jing Li and Wei Jiang, “Detection and Segmentation Text from Natural Scene Images Based on Graph Model”, WSEAS Transactions on Signal Processing, Vol. 10, No. 1, pp. 124-135, 2014.
  • Stanley Sternberg, “Biomedical Image Processing”, IEEE Computer, Vol. 16, No. 1, pp. 22-34, 1983.
  • Ada and Rajneet Kaur, “Feature Extraction and Principal Component Analysis for Lung Cancer Detection in CT Scan Images”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, No. 3, pp. 187-190, 2013.
  • Dixa Saxena, S.K. Saritha and K.N.S.S.V. Prasad, “Survey Paper on Feature Extraction Methods in Text Categorization”, International Journal of Computer Applications, Vol. 166, No. 11, pp. 1-7, 2017.
  • Bin Zhao, Lianru Gao, Wenzhi Liao and Bing Zhang, “A New Kernel Method for Hyperspectral Image Feature Extraction”, Geo-Spatial Information Science, Vol. 20, No. 3, pp. 309-318, 2017.
  • G. Sun, S. Li, Y. Cao and F. Lang, “Cervical Cancer Diagnosis based on Random Forest”, International Journal of Performability Engineering, Vol. 13, No. 4, pp. 446-457, 2017.
  • S. Athinarayanan and M.V. Srinath, “Multi Class Cervical Cancer Classification by using ERSTCM, EMSD and CFE Methods Based Texture Features and Fuzzy Logic Based Hybrid Kernel Support Vector Machine Classifier”, IOSR Journal of Computer Engineering, Vol. 19, No. 1, pp. 23-34, 2017.

Abstract Views: 103

PDF Views: 1




  • Unsupervised Transudative TL Feature Learning for Image Feature Extraction and Representation

Abstract Views: 103  |  PDF Views: 1

Authors

Logeshwari Dhavamani
Department of Information Technology, St. Joseph College of Engineering, India
A. Rajavel
Department of Electronics and Instrumentation Engineering, Kamaraj College of Engineering and Technology, India
Subhadra Mishra
Department of Computer Science and Application, Odisha University of Agriculture and Technology, India
Komal B. Umare
Department of Artificial Intelligence and Machine Learning, Shri Ramdeobaba College of Engineering and Management, India

Abstract


In this study, we address the problem of unsupervised transductive transfer learning for image feature extraction and representation. While transfer learning has shown promising results in various domains, its application to image feature extraction in an unsupervised transductive setting remains relatively unexplored. The research gap lies in the scarcity of methods that can effectively learn meaningful image representations without access to labeled data in the target domain, hindering the broader applicability of transfer learning in computer vision. Our research seeks to bridge this gap by proposing a novel framework that leverages unsupervised feature learning to enhance the adaptability of models across different image domains, thus contributing to the advancement of transfer learning in the field of computer vision. Experimental results demonstrate the effectiveness of our method in addressing this critical research gap and its potential for real-world applications.

Keywords


Unsupervised, Transductive Transfer Learning, Image Feature Extraction, Representation, Deep Neural Networks

References