ICTACT Journal on Image and Video Processing

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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
     

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

Abstract Views: 109  |  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