Content-Based Image Retrieval Using Deep Learning

Authors

  • Dr. J C Karur  Professor, Department of Computer Science and Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka, India
  • Asma Hebbal  Mtech Student Department of Computer Science and Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka, India
  • Dr. Jagadeesh Pujari  HOD Department of Information Science and Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka, India

DOI:

https://doi.org//10.32628/CSEIT228418

Keywords:

Convolutional Neural Networks, VGG16, MobileNet, Cosine Similarity.

Abstract

The most prevalent and well-used method for obtaining images from huge, unlabelled image datasets is content-based image retrieval. Convolutional Neural Networks are pre-trained deep neural networks which can generate and extract accurate features from image databases. These CNN models have been trained using large databases with thousands of classes that include a huge number of images, making it simple to use their information. Based on characteristics retrieved using the pre-trained CNN models, we created CBIR systems in the work. These pre-trained CNN models VGG16, and MobileNet have been employed in this instance to extract sets of features that are afterward saved independently and used for image retrieval.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-08-30

Issue

Volume 8, Issue 4, July-August-2022

Section

Research Articles

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How to Cite

[1]
Dr. J C Karur, Asma Hebbal, Dr. Jagadeesh Pujari, " Content-Based Image Retrieval Using Deep Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.122-128, July-August-2022. Available at doi : https://doi.org/10.32628/CSEIT228418