Machine learning and similar image-based techniques based on Nash game theory

Abstract

The use of computer vision techniques to address the task of image retrieval is known as a Content-Based Image Retrieval (CBIR) system. It is a system designed to locate and retrieve the appropriate digital image from a large database by utilizing a query image. Over the last few years, machine learning algorithms have achieved impressive results in image retrieval tasks due to their ability to learn from large amounts of diverse data and improve their accuracy in image recognition and retrieval. Our team has developed a CBIR system that is reinforced by two machine learning algorithms and employs multiple clustering and low-level image feature extraction, such as color, shape, and texture, to formulate a Nash game. Consequently, we are faced with a multicriteria optimization problem. To solve this problem, we have formulated a three-player static Nash game, where each player utilizes a different strategy (color descriptor, Zernike descriptor, and SFTA descriptor) based on their objective function. The Nash equilibrium is defined as the membership classes of the query image.