Searching for similar images using Nash game and machine learning

The storage of large amounts of digital data, as well as the processing of digital images, are currently expanding significantly across a range of application areas. As a result, effective management of big images databases is necessary, which calls for the employment of automated and cutting-edge i...

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Bibliographic Details
Published inMathematical Modeling and Computing Vol. 11; no. 1; pp. 239 - 249
Main Authors Semmane, F. Z., Moussaid, N., Ziani, M.
Format Journal Article
LanguageEnglish
Published 2024
Online AccessGet full text
ISSN2312-9794
2415-3788
DOI10.23939/mmc2024.01.239

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Summary:The storage of large amounts of digital data, as well as the processing of digital images, are currently expanding significantly across a range of application areas. As a result, effective management of big images databases is necessary, which calls for the employment of automated and cutting-edge indexing techniques. One method used for this is Content-Based Image Retrieval (CBIR), which tries to index and query the picture database using visual aspects of the image rather than its semantic features. In this article, we propose to explore a digital search engine for similar images, based on multiple image representations and clustering, improved by game theory and machine learning methods.
ISSN:2312-9794
2415-3788
DOI:10.23939/mmc2024.01.239