Integration of Machine Learning Algorithms and Nash Game Theory in A Similar Image Retrieval System

In the realm of image processing, the ability to describe an image accurately and comprehensively is paramount. Images, whether captured by cameras, generated by computers, or obtained through various means, contain a wealth of information waiting to be analyzed and understood. Among the key elements defining an image, two stand out prominently: color and form. Each of these components plays a crucial role in shaping our perception and interpretation of visual data. Our team has crafted a CBIR system enhanced by a duo of machine learning algorithms and various clustering techniques alongside low-level image feature extraction methods. These components converge to formulate a Nash game, thereby presenting us with a multi-criteria optimization challenge. In tackling this challenge, we've devised a In this two-player static Nash game, each player employs a unique strategy, specifically color moments and shape moments, corresponding to their objective functions. The Nash equilibrium is represented by the membership classes assigned to the query image. Looking ahead, we intend to replace the Zernike moments with a neural network approach and thoroughly compare the two methods concerning time and efficiency