Content-Based Image Retrieval : A Comprehensive Study

Authors

  • Abhishek Swaroop  Head of Department, Department of Information Technology, Bhagwan Parshuram Institute of Technology, New Delhi, Delhi, India
  • Aman  Student, Department of Information Technology & Engineering, GGSIPU, Bhagwan Parshuram Institute of Technology, Affiliated to GGSIPU, New Delhi, India
  • Amit Rawat  Student, Department of Information Technology & Engineering, GGSIPU, Bhagwan Parshuram Institute of Technology, Affiliated to GGSIPU, New Delhi, India
  • Ashwin Giri  Student, Department of Information Technology & Engineering, GGSIPU, Bhagwan Parshuram Institute of Technology, Affiliated to GGSIPU, New Delhi, India
  • Hardik Gothwal  Student, Department of Information Technology & Engineering, GGSIPU, Bhagwan Parshuram Institute of Technology, Affiliated to GGSIPU, New Delhi, India

DOI:

https://doi.org//10.32628/CSEIT1952275

Keywords:

Content-Based Image Retrieval, Convolutional Neural Networks, Feature Illustration

Abstract

Learning efficient options illustrations and equivalency metric measures are imperative to the searching performance of a content-based image retrieval (CBIR) machine. Despite in depth analysis efforts for many years, it remains one amongst the foremost difficult open issues that significantly hinders the success of real- world CBIR systems. The key issue has been associated to the commonly known “linguistic gap” problem that exists between low-level image pixels captured by machines and high-level linguistics ideas perceived by humans. Among varied techniques, machine learning has been actively investigated as a potential direction to bridge the linguistics gap in the long run. Motivated by recent success of deep learning techniques for computer vision and other applications, In this paper, we'll conceive to address an open problem: if deep learning could be a hope for bridging the linguistics gap in CBIR and the way a lot of enhancements in CBIR tasks may be achieved by exploring the progressive deep learning methodologies for learning options illustrations and equivalency measures. Speci?cally, we'll investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a progressive deep learning technique (Convolutional Neural Networks) for CBIR tasks in varied settings. From our empirical studies, we found some encouraging results and summarized some vital insights for future analysis. CBIR tasks may be achieved by exploring the progressive deep learning techniques for learning options illustrations and equivalency measures.

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

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Published

2019-04-30

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Volume 5, Issue 2, March-April-2019

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Research Articles

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[1]
Abhishek Swaroop, Aman, Amit Rawat, Ashwin Giri, Hardik Gothwal, " Content-Based Image Retrieval : A Comprehensive Study, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.1073-1081, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952275