Deep Learning Perspectives on Efficient Image Matching in Natural Image Databases

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Published: Nov 2, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i10.8479
Keywords:
Deep Learning, Image Matching, Efficient Sampling, Feature Extraction, Real-time Matching, Data Augmentation

Main Article Content

Mirzanur Rahman, D Palanikkumar, Dankan Gowda V, P Ramesh Naidu, Parismita Sarma, Vaishali N. Agme

Abstract

With the proliferation of digital content, efficient image matching in natural image databases has become paramount. Traditional image matching techniques, while effective to a certain extent, face challenges in dealing with the high variability inherent in natural images. This research delves into the application of deep learning models, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and Triplet Networks, to address these challenges. We introduce various techniques to enhance efficiency, such as data augmentation, transfer learning, dimensionality reduction, efficient sampling, and the amalgamation of traditional computer vision strategies with deep learning. Our experimental results, garnered from specific dataset, demonstrate significant improvements in image matching efficiency, as quantified by metrics like precision, recall, F1-Score, and matching time. The findings underscore the potential of deep learning as a transformative tool for natural image database matching, setting the stage for further research and optimization in this domain.

Article Details

How to Cite
Mirzanur Rahman, et al. (2023). Deep Learning Perspectives on Efficient Image Matching in Natural Image Databases. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 172–184. https://doi.org/10.17762/ijritcc.v11i10.8479
Issue
Vol. 11 No. 10 (2023)
Section
Articles
Author Biography

Mirzanur Rahman, D Palanikkumar, Dankan Gowda V, P Ramesh Naidu, Parismita Sarma, Vaishali N. Agme

Mirzanur Rahman1*, D Palanikkumar2, Dankan Gowda V3, P Ramesh Naidu4, Parismita Sarma5, Vaishali N. Agme6

1*Assistant Professor, Department of  Information Technology,  Gauhati University, Guwahati, Assam,  India.

*email: mr@gauhati.ac.in

2Professor, Department of Computer Science and  Engineering, Dr NGP Institute of Technology, Coimbatore, Tamilnadu, India.

email: palanikkumard@gmail.com

3Department of Electronics and Communication Engineering, BMS Institute of Technology and Management, Bangalore, Karnataka, India.

e-mail: dankan.v@bmsit.in  

4Associate Professor, Department of Computer Science and  Engineering, Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India.

e-mail:  ramesh.naidu@nmit.ac.in

5Assistant Professor, Department of  Information Technology,  Gauhati University, Guwahati, Assam,  India.

e-mail:  pari@gauhati.ac.in

6Assistant  Professor, Department of Applied Science,  Bharati Vidyapeeth College of Engineering, Navi Mumbai, Maharashtra, India.

e-mail: vaishali.agme@bvcoenm.edu.in

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