A Deep Instance-Based Learning Model for Content-Based Image Retrieval

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ID: 312726
2025
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Ranked #283 of 705 articles by views in Journal of Computing & Biomedical Informatics

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Abstract
Over a decade, content-based image retrieval has been an active field. It is not possible to compare the performance of two of these systems using objective means. Consequently, finding successful or hopeful ways forward is very challenging which delays the progress of the field. Finding out if a CBIR application is of good quality is tough which influences how well such systems can be commercialized. A severe application cannot be developed or grow commercially unless its reliability can be proved. The TREC metric is frequently used for operations within a text document and TPC is usually used for database processing. Because of the framework in place, systems can now be checked against little, open-to-the-public test databases. This work sets out to build an image retrieval system that uses deep learning to understand the similarity in images belonging to certain classes because of how learnable features and a similarity measure are used, all supported by inception-v3 CNN technology. To achieve simplicity, good retrieval and efficiency, the CNN features with a Siamese design are put to work.
Reference Key
imported_1777056294_69ebba266cb0f Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mamoona Shafique
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/965
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