Performance evaluation of the fast forward quantum optimization algorithm in digital image clustering

The primary objective of clustering in image analysis is to establish a meaningful correspondence between image features and clusters.This process is instrumental in extracting higher-level semantic information from digital images.In this study, we propose a novel image clustering approach that integrates the fast forward quantum optimization algorithm (FFQOA) with the K-means clustering (KMC) algorithm, forming a hybrid method referred to as FFQOA + KMC.The FFQOA + KMC initiates clustering based on the grayscale values of images using KMC and then refines the clustering outcome through FFQOA to achieve optimal segmentation.Subsequently, FFQOA + KMC is applied to several benchmark grayscale images, with results compared to those from alternative clustering techniques.Experimental findings confirm the robustness and superiority of FFQOA + KMC through both visual inspections and statistical metrics.