<![CDATA[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]]>
Copyrights © 2026
