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  4. Searching for similar images using Nash game and machine learning

Searching for similar images using Nash game and machine learning

MMC.
2024;
Volume 11, Number 1
: pp. 239–249
https://doi.org/10.23939/mmc2024.01.239
Received: May 31, 2023
Revised: March 03, 2024
Accepted: March 04, 2024

Semmane F. Z., Moussaid N., Ziani M.  Searching for similar images using Nash game and machine learning.  Mathematical Modeling and Computing. Vol. 11, No. 1, pp. 239–249 (2024)

Authors:
  1. F. Z. Semmane
  2. N. Moussaid
  3. M. Ziani
1
LMCSA, FSTM, Hassan II University of Casablanca; LMSA, Department of Mathematics, Faculty of Sciences, Mohammed V University in Rabat
2
University Hassan II of Casablanca, FST Mohammedia, Laboratory of Mathematics, Computer Science and Applications (LMCSA)
3
LMSA, Department of Mathematics, Faculty of Sciences, Mohammed V University in Rabat

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.

image retrieval
image descriptors
image colors
GIST
Zernike moments
concurrent optimization
Nash game and images clustering
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