concurrent optimization

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 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

Image retrieval using Nash equilibrium and Kalai-Smorodinsky solution

In this paper, we propose a new formulation of Nash games for solving a general multi-objectives optimization problems.  The objective of this approach is to split the optimization variables, allowing us to determine numerically the strategies between two players.  The first player minimizes his function cost using the variables of the first table P and the second player, using the second table Q.  The original contribution of this work concerns the construction of the two tables of allocations that lead to a Nash equilibrium on the Pareto front.  The second proposition of this paper is to